Target area composite high-temperature drought prediction method based on dynamics

Abstract

The invention discloses a target area composite high-temperature drought prediction method based on dynamics, and belongs to the technical field of meteorological disaster prediction. The method comprises the following steps: acquiring a standardized composite high-temperature drought index of a target area in summer in previous years, and acquiring forecast factor indexes of the previous years and Y years to be forecasted; inputting the forecast factor index of the Y forecast year into a forecast model of forecast quantity DPI pre-constructed based on the forecast factor index of the previous year, and predicting a DPI forecast value of the Y year; adding the composite high-temperature drought index of the Y-1 year and the predicted DPI predicted value of the Y year to obtain a composite high-temperature drought index predicted value of the Y year of the predicted year; according to the method, the early-stage forecasting factor index of the monthly scale is taken as a forecasting factor to be incorporated into the composite high-temperature drought forecasting model, the composite high-temperature drought condition of the target area can be forecasted at least one month ahead of time, and the forecasting time efficiency is greatly improved while the forecasting effect is ensured.

Description

Technical Field

[0001] The invention relates to a target area composite high temperature drought prediction method based on dynamics, and belongs to the technical field of meteorological disaster prediction. Background Art

[0002] Temperature changes caused by global warming have played an increasingly important role in the occurrence of drought events. In recent years, the frequency of complex high-temperature drought events has increased significantly.

[0003] In the context of global warming, frequent high temperature and drought have had a profound impact on human society and natural ecosystems. In the field of agricultural production, high temperature and drought not only hinder the growth and development of crops, significantly reduce yield and quality, but also seriously affect the normal production of cash crops such as tea and tobacco. In terms of water resources, high temperature accelerates the evaporation of surface water, causing the groundwater level to continue to decline. At the same time, water quality deteriorates due to increased pollutant concentrations, further exacerbating the shortage and pollution of water resources. In addition, high temperature and drought also destroy the balance of the ecosystem, reduce biodiversity, and provide opportunities for the invasion of alien species. For human society, high temperature and drought not only restrict the daily life of residents, affect industrial production, but also increase health risks. In order to cope with the challenges brought by high temperature and drought events, a large number of studies have focused on the changing characteristics, physical mechanisms and construction of prediction models of high temperature and drought.

[0004] The prior art uses the interannual increment method to predict summer temperatures. This method calculates the increments of temperature between adjacent years and uses these increments as forecast quantities to predict temperature changes in future years. The prior art uses two numerical models, WRF (Weather Research and Forecasting Model) and CCSM4 (Community Climate SystemModel version 4), to predict summer precipitation. The prior art predicts the frequency of extreme precipitation in summer based on changes in sea temperature and sea ice in South India. Most of the prediction models for high-temperature droughts that have been applied for and approved are for single events. For example, Chinese patent application CN112330075A discloses a method for predicting China's four-season temperature based on the coordinated changes of the East Asian subtropical jet and the polar front jet; Chinese patent application CN118503655A discloses a method and system for predicting drought in a changing environment based on an improved ensemble prediction method. In addition, some patents use meteorological elements with a time range close to the prediction period as prediction factors to build models. For example, Chinese patent application CN118962847A discloses a method for predicting plateau agricultural drought, which predicts the degree of drought in plateau agricultural areas through precipitation, temperature, air humidity and wind intensity in plateau planting areas. At the same time, there are also more and more models using artificial intelligence technology for prediction. For example, Chinese patent application CN118916695A discloses an AI prediction method for global drought spatiotemporal changes, and Chinese patent application CH117741822A discloses a temperature prediction method and device based on graph neural network.

[0005] However, most existing models focus on predicting drought through a single variable such as less precipitation, or only predict single events such as high temperature or drought, and lack the ability to predict complex high temperature and drought events. Models built using meteorological elements with a time range close to the prediction period as prediction factors have shortcomings in terms of prediction timeliness. Although prediction models based on artificial intelligence have advantages such as fast prediction speed and strong ability to process big data, they lack the addition of relevant physical mechanisms, and the reliability of the model needs further verification. Dynamic statistical models are used in a variety of climate prediction models, which specifically refer to statistical models based on the understanding of physical mechanisms.

[0006] Among the currently proposed high temperature and drought prediction models, some models only focus on a single variable for prediction, fail to cover the complexity of compound high temperature and drought events, and lack the ability to comprehensively predict such events. Summary of the invention

[0007] The purpose of the present invention is to overcome the deficiencies of the prior art and provide a target area composite high temperature drought prediction method based on dynamics.

[0008] To achieve the above object / To solve the above technical problems, the present invention is implemented by the following technical solutions: A method for predicting compound high temperature and drought in a target area based on dynamics, the method comprising: Obtain the standardized compound high temperature and drought index ZPIJJA_REGION in the target area in summer of year Y - 1 Y-1 ; Obtain the predictor factor indices for previous years and the forecast year Y, including sea ice concentration index, sea surface temperature index, and soil moisture index; Input the predictor factor indices of the forecast year Y obtained into a prediction model of the predicted quantity DPI pre - constructed based on the predictor factor indices of previous years, and predict the DPI prediction value for year Y; Add the compound high temperature and drought index ZPIJJA_REGION Y-1 to the predicted DPI prediction value for year Y to obtain the predicted value of the compound high temperature and drought index ZPIJJA_REGION for the forecast year Y Y ; wherein, Y is the year of compound high temperature and drought in the target area to be predicted.

[0009] Optionally, obtaining the standardized compound high temperature and drought index ZPIJJA_REGION in the target area in summer of year Y - 1 Y-1 , includes: Collect monthly gridded temperature and precipitation data in the target area from year Y - N to year Y - 1 before the forecast year Y through the CN05.1 dataset, and the horizontal resolution of the data is 0.25° 0.25°; N is the time span of data collection, in years; According to the monthly gridded temperature and precipitation data, based on the joint cumulative probability density function, use the t - coupla method to construct the compound high temperature and drought index PI representing the intensity of compound high temperature and drought in the target area during the period from year Y - N to year Y - 1; Extract the indices of June, July, and August of each year from the compound high temperature and drought index PI of each of the 12 months from year Y - N to year Y - 1, and after averaging in the month dimension, obtain the data PIJJA representing the intensity of compound high temperature and drought in summer in the target area; Standardize the compound high temperature and drought data PIJJA in summer in the target area from year Y - N to year Y - 1 to obtain the standardized compound high temperature and drought index ZPIJJA_REGION in summer in the target area from year Y - N to year Y - 1.

[0010] Optionally, constructing the compound high temperature and drought index PI representing the intensity of compound high temperature and drought in the target area during the period from year Y - N to year Y - 1, includes: Construct the cumulative probability density function of temperature and precipitation , where the calculation formulas for the cumulative probability density functions of air temperature and precipitation are as follows: ; In the formula: is the cumulative probability density function of air temperature and precipitation. For i = 1, 2, when i = 1, is the cumulative probability density function of air temperature. When i = 2, is the cumulative probability density function of precipitation; P is the calculation process of the cumulative probability density function, is the cumulative distribution of high-temperature events or precipitation shortage events. For i = 1, 2, when i = 1, is the cumulative distribution of high-temperature events. When i = 2, is the cumulative distribution of precipitation shortage events; The individual return periods of high-temperature events and precipitation shortage events are calculated respectively through the cumulative probability density functions of air temperature and precipitation , where the calculation formulas for the individual return periods of high-temperature events and precipitation shortage events are as follows: ; In the formula: is the individual return period of high-temperature events or precipitation shortage events. For i = 1, 2, when i = 1, is the individual return period of high-temperature events. When i = 2, is the individual return period of precipitation shortage events; is the calculated cumulative probability density function of air temperature and precipitation; Based on the obtained cumulative probability density distribution functions of air temperature and precipitation the cumulative probability density distribution functions of the survival of air temperature and precipitation are calculated , and its calculation formula is as follows: ; In the formula: is the cumulative probability density distribution function of the survival of air temperature and precipitation. For i = 1, 2, when i = 1, is the cumulative probability density distribution function of the survival of air temperature. When i = 2, is the cumulative probability density distribution function of the survival of precipitation; is the cumulative probability density function of air temperature and precipitation. For i = 1, 2, when i = 1, is the cumulative probability density function of air temperature. When i = 2, is the cumulative probability density function of precipitation; Using the t-copula method, the joint survival cumulative probability density function of compound high temperature and drought events is calculated based on the calculated survival cumulative probability density distribution functions of air temperature and precipitation, and the joint survival cumulative probability density function is defined as the compound high temperature and drought index PI characterizing the intensity of compound high temperature and drought. The calculation formula is as follows: ; In the formula: PI is the defined compound high temperature and drought index; C is the process of calculating the compound high temperature and drought index by the t-copula method, is the survival cumulative probability density function of air temperature, is the survival cumulative probability density function of precipitation; P is the calculation process of the cumulative probability density function, is the joint cumulative distribution of high temperature events and precipitation shortage events.

[0011] Optionally, the predicted DPI forecast value for year Y includes: Calculating the standardized forecast factor interannual increment index for year Y, with the forecast year being year Y; After all the forecast factor indexes are calculated, the forecast factor indexes for year Y are input into the forecast model of the forecast quantity DPI constructed based on the forecast factor indexes of previous years to obtain the DPI forecast value for year Y. The calculation formula is as follows: ; In the formula: Y is the forecast year, a, b, and c are regression coefficients, d is the regression constant, DPI Y is the forecast value of the standardized compound high temperature and drought interannual increment index for the summer of year Y in the target area, x1 Y , x2 Y , x3 Y are the various forecast factor indexes for year Y.

[0012] Optionally, obtaining the predicted value ZPIJJA_REGION of the compound high temperature and drought index for the forecast year Y Y , includes: Adding the forecast value DPI of the standardized compound high temperature and drought interannual increment index for the summer of year Y in the predicted target area Y to the standardized compound high temperature and drought index ZPIJJA_REGION for the summer of year Y - 1 in the target area Y-1 to obtain the predicted value ZPIJJA_REGION for the forecast year Y Y , and the calculation formula is as follows: ; In the formula: Y is the forecast year, ZPIJJA_REGION Y is the forecast value of the standardized compound high temperature and drought index for the summer of year Y in the target area, DPI Yis the predicted value of the standardized composite high temperature and drought inter-annual increment index for the summer of year Y in the target region, ZPIJJA_REGION Y-1 is the true value of the standardized composite high temperature and drought index for the summer of year Y-1 in the target region.

[0013] Optionally, the method for constructing the prediction model of the predicted quantity DPI includes: According to the standardized composite high temperature and drought index ZPIJJA_REGION in the target region from year Y-N to year Y-1 in summer, the standardized composite high temperature and drought inter-annual increment index DPI from year Y-N-1 to year Y-1 in summer in the target region is defined by the inter-annual increment method, and its calculation formula is as follows: ; In the formula: y is the year, DPI is the standardized composite high temperature and drought inter-annual increment index from year Y-N-1 to year Y-1 in summer in the target region, and ZPIJJA_REGION is the standardized composite high temperature and drought index from year Y-N to year Y-1 in summer in the target region; Taking the sea ice concentration index, sea surface temperature index, and soil moisture index from year Y-N-1 to year Y-1 in previous years as the predictor factor indices, and DPI as the predicted quantity, after confirming the independence between the predictor factors, a prediction model for the predicted quantity DPI is constructed based on the multiple linear regression method, where the prediction equation is: ; In the formula: y is the year, a, b, and c are regression coefficients, d is the regression constant, DPI is the predicted value of the standardized composite high temperature and drought inter-annual increment index from year Y-N-1 to year Y-1 in summer in the target region, and x1, x2, and x3 are the defined predictor factor indices.

[0014] Optionally, the method for obtaining the predictor factor indices of previous years and the year Y to be predicted includes: Collecting the monthly sea ice concentration SIC and sea surface temperature SST data of the world from year Y-N to year Y through the Hadley dataset, and the data horizontal resolution is 1° 1°; Collecting the monthly soil moisture SW data of the world from year Y-N to year Y through the ERA5 dataset, and the data horizontal resolution is 1° 1°; N is the time span of data collection, in years; Using the inter-annual increment method to process the sea ice concentration SIC, sea surface temperature SST, and soil moisture SW from year Y-N to year Y to obtain the inter-annual increment values of sea ice concentration Dsic, sea surface temperature Dsst, and soil moisture Dsw in the form of inter-annual increment, and their calculation formulas are as follows: ; Where: y is the year, m is the month, m = 1, 2, 3,..., 12, t is the t-th latitude, t = 1, 2, 3,..., 181, p is the p-th longitude, p = 1, 2, 3,..., 360; DXXX is the data of the previous meteorological factor in the form of interannual increment, DXXX = DSIC, DSST, DSW, XXX is the data of the previous meteorological factor, XXX = SIC, SST, SW; Based on the interannual increment values of sea ice concentration DSIC, sea surface temperature DSST, and soil moisture DSW in the form of interannual increment, the sea ice concentration index, sea surface temperature index, and soil moisture index are calculated.

[0015] Optionally, the method for obtaining the sea ice concentration index includes: Extracting the sea ice key month from the monthly scale of the interannual increment value of sea ice concentration DSIC of the sea ice concentration data SICM, and its calculation formula is as follows: ; Where: y is the year, t is the t-th latitude, t = 1, 2, 3,..., 181, p is the p-th longitude, p = 1, 2, 3,..., 360, SICM(y, t, p) is the interannual increment value of the sea ice concentration at the grid point of the t-th latitude and the p-th longitude in the y-th month, DSIC(y, , t, p) is the interannual increment value of the sea ice concentration at the grid point of the t-th latitude and the p-th longitude in the y-th month, = 1, 2, 3,..., 12; On the basis of the interannual increment value SICM of the sea ice concentration, the SICM where the latitude grid point and the longitude grid point are both within the sea ice concentration key area range is extracted and averaged to obtain the interannual increment value SICBS of the sea ice concentration in the sea ice concentration key area, and its calculation formula is as follows: ; Where: y is the year, n1 is the number of grid points where the longitude and latitude fall within the sea ice concentration key area, (ti, pi) is the i-th grid point where the longitude and latitude fall within the sea ice concentration key area, i = 1, 2, 3,..., n1, SICBS is the interannual increment value of the sea ice concentration in the sea ice concentration key area, SICM(y, ti, pi) is the sea ice concentration value in the y-th month of the i-th falling within the sea ice concentration key area; Performing standardization processing on the interannual increment value SICBS of the sea ice concentration in the sea ice concentration key area to obtain the sea ice concentration index, and its calculation formula is as follows: ; Where: y is the year, ZSICBS is the sea ice concentration index, Z is the standardization process, SICBS is the interannual increment value of the sea ice concentration in the key sea ice concentration area, μ(SICBS) is the average value of SICBS from year (Y - N - 1) to year Y, and σ(SICBS) is the standard deviation of SICBS from year (Y - N - 1) to year Y.

[0016] Optionally, the method for obtaining the sea surface temperature index includes: Extract the sea surface temperature key months from the monthly scale of the interannual increment value DSST of the sea surface temperature of the sea surface temperature data SSTM, and its calculation formula is as follows: ; Where: y is the year, t is the t-th latitude, t = 1, 2, 3,..., 181, p is the p-th longitude, p = 1, 2, 3,..., 360, SSTM(y, t, p) is the interannual increment value of the sea surface temperature at the grid point of the t-th latitude and the p-th longitude in year y month, DSST(y, , t, p) is the interannual increment value of the sea surface temperature at the grid point of the t-th latitude and the p-th longitude in year y month, = 1, 2, 3,..., 12; Based on the interannual increment value SSTM of the sea surface temperature, extract and average the SSTM where the latitude grid point and the longitude grid point are both within the sea surface temperature key area to obtain the interannual increment value SSTBS of the sea surface temperature in the sea surface temperature key area, and its calculation formula is as follows: ; Where: y is the year, n2 is the number of grid points where the longitude and latitude fall within the sea surface temperature key area, (ti, pi) is the i-th grid point where the longitude and latitude fall within the sea surface temperature key area, i = 1, 2, 3,..., n2, SSTBS is the interannual increment value of the sea surface temperature in the sea surface temperature key area, and SSTM(y, ti, pi) is the sea surface temperature value in year y month at the i-th grid point falling within the sea surface temperature key area; Perform standardization processing on the interannual increment value SSTBS of the sea surface temperature in the sea surface temperature key area to obtain the sea surface temperature index, and its calculation formula is as follows: ; Where: y is the year, ZSSTBS is the sea surface temperature index, Z is the standardization process, SSTBS is the interannual increment value of the sea surface temperature in the sea surface temperature key area, μ(SSTBS) is the average value of SSTBS from year (Y - N - 1) to year Y, and σ(SSTBS) is the standard deviation of SSTBS from year (Y - N - 1) to year Y.

[0017] Optionally, the method for obtaining the soil moisture index includes: Extract the key months of soil moisture from the monthly scale of the annual increment value DSW of soil moisture The soil moisture data SWM is calculated as follows: ; In the formula: y is the year, t is the t-th latitude, t = 1, 2, 3,..., 181, p is the p-th longitude, p = 1, 2, 3,..., 360, SWM(y, t, p) is the annual increment value of soil moisture at the grid point of the t-th latitude and the p-th longitude in year y for the month, DSW(y, , t, p) is the annual increment value of soil moisture at the grid point of the t-th latitude and the p-th longitude in year y for the month, = 1, 2, 3,..., 12; Based on the annual increment value SWM of soil moisture, the SWM index where both the latitude grid point and the longitude grid point are within the range of the soil moisture key area is extracted and averaged to obtain the annual increment value SWBS of soil moisture in the soil moisture key area. The calculation formula is as follows: ; In the formula: y is the year, n3 is the number of grid points where the longitude and latitude fall within the soil moisture key area, (ti, pi) is the i-th grid point where the longitude and latitude fall within the soil moisture key area, i = 1, 2, 3,..., n1, SWBS is the annual increment value of soil moisture in the soil moisture key area, SWM(y, ti, pi) is the soil moisture value in year y for the month; The annual increment value SWBS of soil moisture in the soil moisture key area is standardized to obtain the soil moisture index. The calculation formula is as follows: ; In the formula: y is the year, ZSWBS is the soil moisture index, Z is the standardization process, SWBS is the annual increment value of soil moisture in the soil moisture key area, μ(SWBS) is the average value of SWBS from year Y - N - 1 to year Y, and σSWBS is the standard deviation of SWBS from year (Y - N - 1) to year Y.

[0018] Compared with the prior art, the beneficial effects achieved by the present invention are: The present invention combines the survival cumulative probability density function, adopts the t-coupla method, uses the monthly data of temperature and precipitation to calculate and define the monthly gridded data of the composite high-temperature drought index PI, takes the interannual increment form of the composite high-temperature drought index DPI as the forecasting quantity, screens out the antecedent meteorological factors affecting the composite high-temperature drought situation as the forecasting factors, and predicts the composite high-temperature drought event; the present invention incorporates the antecedent forecasting factor index at the monthly scale into the composite high-temperature drought forecasting model, can forecast the composite high-temperature drought situation in the target area at least 1 month in advance, and has a great improvement in the forecasting timeliness while ensuring the forecasting effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a diagram showing the scope of the target area in the embodiment of the present invention; Figure 2 It is a diagram showing the scope of the key area for defining the Barents Sea ice index in the embodiment of the present invention; Figure 3 It is a diagram showing the scope of the key area for defining the La Nina-like sea surface temperature index in the embodiment of the present invention; Figure 4 It is a diagram showing the scope of the key area for defining the soil moisture index in northwestern Siberia in the embodiment of the present invention; Figure 5 It is a diagram showing the correlation coefficients of (a) DPI, (b) the predicted values (dashed lines) and the true values (solid lines) of ZPINEC of the composite high-temperature drought index forecasting model for the target area in 2019 constructed using the data from 1961 to 2018 in the embodiment of the present invention; Figure 6 It is a schematic flow diagram for predicting the composite high-temperature drought in the Northeast Region in the embodiment of the present invention; Figure 7 It is a schematic flow diagram for predicting the composite high-temperature drought in the target area in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] In order to make the technical means, creative features, achieved purposes and effects of the present invention easy to understand, the present invention will be further described below in conjunction with the specific embodiments.

[0021] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "center", "longitudinal", "transverse", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to the present invention. In addition, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first", "second", etc. may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise specified, the meaning of "plural" is two or more.

[0022] In the description of the present invention, it should be noted that unless otherwise clearly specified and defined, the terms "install", "connect", "connection" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood through specific circumstances.

[0023] As Figures 1 - 7 shown, a method for predicting compound high temperature and drought in a target area based on dynamics is disclosed. The method includes: A method for predicting compound high temperature and drought in a target area based on dynamics, the method includes: Step 1, obtain the standardized compound high temperature and drought index ZPIJJA_REGION of the target area in summer of year Y - 1 Y-1 ; Step 2, obtain the predictor factor indices of previous years and the prediction year Y, including sea ice concentration index, sea surface temperature index, and soil moisture index; Step 3, input the predictor factor indices of the prediction year Y obtained into the prediction model of the predicted quantity DPI pre - constructed based on the predictor factor indices of previous years to predict the DPI prediction value of year Y; Step 4, add the compound high temperature and drought index ZPIJJA_REGION Y-1 to the predicted DPI prediction value of year Y to obtain the predicted value of the compound high temperature and drought index ZPIJJA_REGION of the prediction year Y Y ; wherein, Y is the year of compound high temperature and drought in the target area to be predicted.

[0024] Specific steps of Step 1: Obtain the standardized composite high temperature and drought index ZPIJJA_REGION for the summer in the target region in year Y - 1 Y-1 , including: Collect monthly gridded temperature and precipitation data for the target region from year Y - N to year Y - 1 before the forecast year Y through the CN05.1 dataset, with a horizontal resolution of 0.25° 0.25°; N is the time span for data collection, in years; Based on the monthly gridded temperature and precipitation data, adopt the t - copula method to construct the composite high temperature and drought index PI representing the composite high temperature and drought intensity in the target region during the period from year Y - N to year Y - 1 based on the joint cumulative probability density function; Extract the indexes for June, July, and August each year from the composite high temperature and drought index PI for 12 months each year from year Y - N to year Y - 1, and obtain the data PIJJA representing the composite high temperature and drought intensity in summer in the target region after averaging over the month dimension; After standardizing the composite high temperature and drought data PIJJA for the summer in the target region from year Y - N to year Y - 1, obtain the standardized composite high temperature and drought index ZPIJJA_REGION for the summer in the target region from year Y - N to year Y - 1.

[0025] Specific method for model construction in Step 3: Construct the composite high temperature and drought index PI representing the composite high temperature and drought intensity in the target region during the period from year Y - N to year Y - 1, including: Construct the cumulative probability density function of temperature and precipitation , where the calculation formula for the cumulative probability density function of temperature and precipitation is as follows: ; In the formula: is the cumulative probability density function of temperature and precipitation, i = 1, 2, where when i = 1, is the cumulative probability density function of temperature, and when i = 2, is the cumulative probability density function of precipitation; P is the calculation process of the cumulative probability density function, is the cumulative distribution of high - temperature events or precipitation - deficit events, i = 1, 2, where when i = 1, is the cumulative distribution of high - temperature events, and when i = 2, is the cumulative distribution of precipitation - deficit events; Calculate the individual return periods of high - temperature events and precipitation - deficit events respectively through the cumulative probability density functions of temperature and precipitation , where the calculation formulas for the individual return periods of high - temperature events and precipitation - deficit events are as follows: ; In the formula: is the individual return period of the high-temperature event or precipitation shortage event, where i = 1, 2. When i = 1, is the individual return period of the high-temperature event. When i = 2, is the individual return period of the precipitation shortage event; is the calculated cumulative probability density function of temperature and precipitation; Based on the obtained cumulative probability density distribution functions of temperature and precipitation calculate the survival cumulative probability density distribution functions of temperature and precipitation , and its calculation formula is as follows: ; In the formula: is the survival cumulative probability density distribution function of temperature and precipitation, where i = 1, 2. When i = 1, is the survival cumulative probability density distribution function of temperature. When i = 2, is the survival cumulative probability density distribution function of precipitation; is the cumulative probability density function of temperature and precipitation, where i = 1, 2. When i = 1, is the cumulative probability density function of temperature. When i = 2, is the cumulative probability density function of precipitation; Adopt the t-copula method to calculate the joint survival cumulative probability density function of the compound high-temperature and drought event according to the calculated survival cumulative probability density distribution functions of temperature and precipitation, and define the joint survival cumulative probability density function as the compound high-temperature and drought index PI representing the intensity of the compound high-temperature and drought. Its calculation formula is as follows: ; In the formula: PI is the defined compound high-temperature and drought index; C is the process of calculating the compound high-temperature and drought index by the t-copula method, is the survival cumulative probability density function of temperature, is the survival cumulative probability density function of precipitation; P is the calculation process of the cumulative probability density function, is the joint cumulative distribution of the high-temperature event and the precipitation shortage event.

[0026] The specific method for the DPI forecast value in step 3 includes: The predicted DPI forecast value for year Y includes: Calculate the standardized forecast factor annual increment index for year Y, and the forecast year is year Y; After all the forecast factor indexes are calculated, input the forecast factor index for year Y into the forecast model of the forecast quantity DPI constructed based on the previous-year forecast factor indexes to obtain the DPI forecast value for year Y. Its calculation formula is as follows: ; In the formula: Y is the forecast year, a, b, and c are regression coefficients, d is the regression constant, and DPI Y is the predicted value of the standardized composite high temperature and drought inter-annual increment index in the summer of year Y in the target area, and x1 Y , x2 Y , x3 Y are the index values of each prediction factor in year Y.

[0027] Specifically for step 4: obtaining the predicted value ZPIJJA_REGION of the composite high temperature and drought index in the forecast year Y Y , including: Adding the predicted value DPI of the standardized composite high temperature and drought inter-annual increment index in the summer of year Y in the target area Y to the standardized composite high temperature and drought index ZPIJJA_REGION in the summer of year Y - 1 in the target area Y-1 to obtain the predicted value ZPIJJA_REGION in the forecast year Y Y , and its calculation formula is as follows: ; In the formula: Y is the forecast year, ZPIJJA_REGION Y is the predicted value of the standardized composite high temperature and drought index in the summer of year Y in the target area, DPI Y is the predicted value of the standardized composite high temperature and drought inter-annual increment index in the summer of year Y in the target area, and ZPIJJA_REGION Y-1 is the true value of the standardized composite high temperature and drought index in the summer of year Y - 1 in the target area.

[0028] The construction method of the prediction model for the prediction quantity DPI includes: According to the standardized composite high temperature and drought index ZPIJJA_REGION in the summer from year Y - N to year Y - 1 in the target area, the standardized composite high temperature and drought inter-annual increment index DPI from year Y - N - 1 to year Y - 1 in the target area is defined by the inter-annual increment method, and its calculation formula is as follows: ; In the formula: y is the year, DPI is the standardized composite high temperature and drought inter-annual increment index from year Y - N - 1 to year Y - 1 in the target area, and ZPIJJA_REGION is the standardized composite high temperature and drought index from year Y - N to year Y - 1 in the target area; Taking the sea ice concentration index, sea surface temperature index, and soil moisture index from years Y - N - 1 to Y - 1 in previous years as the predictor factor indices, and DPI as the predicted variable, after confirming the independence among the predictor factors, a prediction model for the predicted variable DPI is constructed based on the multiple linear regression method. The prediction equation is as follows: ; In the formula: y is the year, a, b, c are regression coefficients, d is the regression constant, DPI is the predicted value of the standardized composite high - temperature drought inter - annual increment index in the summer from year Y - N - 1 to Y - 1 in the target area, and x1, x2, x3 are the defined predictor factor indices.

[0029] Specifically elaborating on step 2, the method for obtaining the predictor factor indices of previous years and the predicted year Y includes: Collect the monthly sea ice concentration SIC and sea surface temperature SST data of the whole world from year Y - N to year Y through the Hadley dataset, and the horizontal resolution of the data is 1° 1°; Collect the monthly soil moisture SW data of the whole world from year Y - N to year Y through the ERA5 dataset, and the horizontal resolution of the data is 1° 1°; N is the time span of data collection, with the unit of year; Using the inter - annual increment method to process the sea ice concentration SIC, sea surface temperature SST, and soil moisture SW from year Y - N to year Y, the sea ice concentration inter - annual increment value DSIC, sea surface temperature inter - annual increment value DSST, and soil moisture inter - annual increment value DSW in the form of inter - annual increment are obtained. Their calculation formulas are as follows: ; In the formula: y is the year, m is the month, m = 1, 2, 3,..., 12, t is the t - th latitude, t = 1, 2, 3,..., 181, p is the p - th longitude, p = 1, 2, 3,..., 360; DXXX is the data of the previous meteorological factor in the form of inter - annual increment, DXXX = DSIC, DSST, DSW, and XXX is the data of the previous meteorological factor, XXX = SIC, SST, SW; Based on the sea ice concentration inter - annual increment value DSIC, sea surface temperature inter - annual increment value DSST, and soil moisture inter - annual increment value DSW in the form of inter - annual increment, calculate the sea ice concentration index, sea surface temperature index, and soil moisture index; The method for obtaining the sea ice concentration index includes: Extract the sea ice concentration data SICM of the key month from the monthly scale of the sea ice concentration inter - annual increment value DSIC, and its calculation formula is as follows: ; ​Where: y is the year, t is the t-th latitude, t = 1, 2, 3,..., 181, p is the p-th longitude, p = 1, 2, 3,..., 360, and SICM(y, t, p) is the interannual increment value of sea ice concentration at the grid point of the t-th latitude and the p-th longitude in year y for the month. DSIC(y, , t, p) is the interannual increment value of sea ice concentration at the grid point of the t-th latitude and the p-th longitude in year y for the month, = 1, 2, 3,..., 12; Based on the interannual increment value SICM of sea ice concentration, SICM where both the latitude grid point and the longitude grid point are within the sea ice concentration key area is extracted and averaged to obtain the interannual increment value SICBS of sea ice concentration in the sea ice concentration key area. The calculation formula is as follows: ; Where: y is the year, n1 is the number of grid points where the longitude and latitude fall within the sea ice concentration key area, (ti, pi) is the i-th grid point where the longitude and latitude fall within the sea ice concentration key area, i = 1, 2, 3,..., n1, SICBS is the interannual increment value of sea ice concentration in the sea ice concentration key area, and SICM(y, ti, pi) is the sea ice concentration value in year y of the i-th grid point that falls within the sea ice concentration key area for the month; The interannual increment value SICBS of sea ice concentration in the sea ice concentration key area is standardized to obtain the sea ice concentration index. The calculation formula is as follows: ; Where: y is the year, ZSICBS is the sea ice concentration index, Z is the standardization process, SICBS is the interannual increment value of sea ice concentration in the sea ice concentration key area, μ(SICBS) is the average value of SICBS from year (Y - N - 1) to year Y, and σ(SICBS) is the standard deviation of SICBS from year (Y - N - 1) to year Y

[0030] The method for obtaining the sea surface temperature index includes: Extracting the sea surface temperature data SSTM of the key months of sea surface temperature from the monthly scale of the interannual increment value DSST of sea surface temperature. The calculation formula is as follows: ; Where: y is the year, t is the t-th latitude, t = 1, 2, 3,..., 181, p is the p-th longitude, p = 1, 2, 3,..., 360, and SSTM(y, t, p) is the interannual increment value of sea surface temperature at the grid point of the t-th latitude and the p-th longitude in year y for the month. DSST(y, ,(t, p) is the interannual increment value of sea surface temperature in the grid point at the t-th latitude and p-th longitude in year y for the month, = 1, 2, 3,..., 12; Based on the interannual increment value of sea surface temperature SSTM, the SSTM values where both the latitude grid point and the longitude grid point are within the sea surface temperature key area are extracted and averaged to obtain the interannual increment value of sea surface temperature SSTBS in the sea surface temperature key area. The calculation formula is as follows: ; In the formula: y is the year, n2 is the number of grid points where the longitude and latitude fall within the sea surface temperature key area, (ti, pi) is the i-th grid point where the longitude and latitude fall within the sea surface temperature key area, i = 1, 2, 3,..., n2, SSTBS is the interannual increment value of sea surface temperature in the sea surface temperature key area, and SSTM(y, ti, pi) is the sea surface temperature value in the i-th grid point that falls within the sea surface temperature key area in year y for the month; The interannual increment value of sea surface temperature SSTBS in the sea surface temperature key area is standardized to obtain the sea surface temperature index. The calculation formula is as follows: ; In the formula: y is the year, ZSSTBS is the sea surface temperature index, Z is the standardization process, SSTBS is the interannual increment value of sea surface temperature in the sea surface temperature key area, μ(SSTBS) is the average value of SSTBS from year (Y - N - 1) to year Y, and σ(SSTBS) is the standard deviation of SSTBS from year (Y - N - 1) to year Y.

[0031] The method for obtaining the soil moisture index includes: Extract the soil moisture data SWM of the key months of soil moisture from the monthly scale of the interannual increment value of soil moisture DSW, and its calculation formula is as follows: ; In the formula: y is the year, t is the t-th latitude, t = 1, 2, 3,..., 181, p is the p-th longitude, p = 1, 2, 3,..., 360, SWM(y, t, p) is the interannual increment value of soil moisture in the grid point at the t-th latitude and p-th longitude in year y for the month, DSW(y, , t, p) is the interannual increment value of soil moisture in the grid point at the t-th latitude and p-th longitude in year y for the month, = 1, 2, 3,..., 12; Based on the interannual increment value of soil moisture SWM, the SWM values where both the latitude grid point and the longitude grid point are within the soil moisture key area are extracted and averaged to obtain the interannual increment value of soil moisture SWBS in the soil moisture key area. The calculation formula is as follows: ; In the formula: y is the year, n3 is the number of grid points where the longitude and latitude fall within the critical soil moisture area, (ti, pi) is the i-th grid point where the longitude and latitude fall within the critical soil moisture area, i = 1, 2, 3,..., n1, SWBS is the interannual increment value of soil moisture in the critical soil moisture area, and SWM(y, ti, pi) is the soil moisture value in the y-th year of the i-th grid point falling within the critical soil moisture area month; The interannual increment value SWBS of soil moisture in the critical soil moisture area is standardized to obtain a soil moisture index, and its calculation formula is as follows: ; In the formula: y is the year, ZSWBS is the soil moisture index, Z is the standardization process, SWBS is the interannual increment value of soil moisture in the critical soil moisture area, μ(SWBS) is the average value of SWBS from year Y - N - 1 to year Y, and σSWBS is the standard deviation of SWBS from year (Y - N - 1) to year Y.

[0032] For this embodiment, monthly temperature, precipitation, sea ice concentration data, sea surface temperature data, and soil moisture data from 1963 to 2022 are used, plus global sea ice concentration data for March 2023, global sea temperature data for February 2023, and soil moisture data for the Siberian region in April 2023 to predict the composite high-temperature drought situation in Northeast China in the summer of 2023, and a detailed description is given in combination with specific regions: Step 1: Obtain the standardized composite high-temperature drought index for the summer of 2022 in Northeast China ; Collect monthly gridded temperature and precipitation data for Northeast China from 1963 to 2022 through the CN05.1 dataset, and the horizontal resolution of the data is 0.25° 0.25°; Based on the monthly gridded temperature and precipitation data from 1963 to 2022, construct the cumulative probability density function of temperature and precipitation , and the calculation formula of the cumulative probability density function of temperature and precipitation is as follows: ; In the formula: is the cumulative probability density function of temperature and precipitation, i = 1, 2. When i = 1, is the cumulative probability density function of temperature. When i = 2, is the cumulative probability density function of precipitation; P is the calculation process of the cumulative probability density function, is the cumulative distribution of high-temperature events or precipitation deficiency events, i = 1, 2. When i = 1, is the cumulative distribution of high-temperature events. When i = 2, is the cumulative distribution of precipitation deficit events; Calculate the individual return periods of high-temperature events and precipitation deficit events through the cumulative probability density functions of temperature and precipitation respectively, where the calculation formulas for the individual return periods of high-temperature events and precipitation deficit events are as follows: ; In the formula: is the individual return period of high-temperature events or precipitation deficit events, i = 1, 2. When i = 1, is the individual return period of high-temperature events. When i = 2, is the individual return period of precipitation deficit events; is the calculated cumulative probability density function of temperature and precipitation; Based on the obtained cumulative probability density distribution functions of temperature and precipitation Calculate the survival cumulative probability density distribution functions of temperature and precipitation , and its calculation formula is as follows: ; In the formula: is the survival cumulative probability density distribution function of temperature and precipitation, i = 1, 2. When i = 1, is the survival cumulative probability density distribution function of temperature. When i = 2, is the survival cumulative probability density distribution function of precipitation; is the cumulative probability density function of temperature and precipitation, i = 1, 2. When i = 1, is the cumulative probability density function of temperature. When i = 2, is the cumulative probability density function of precipitation; Adopt the t-copula method to calculate the joint survival cumulative probability density function of compound high-temperature and drought events based on the calculated survival cumulative probability density distribution functions of temperature and precipitation, and define the joint survival cumulative probability density function as the compound high-temperature and drought index PI representing the intensity of compound high-temperature and drought in Northeast China during the period from 1963 to 2022. Its calculation formula is as follows: ; In the formula: PI is the compound high-temperature and drought index representing the intensity of compound high-temperature and drought in Northeast China during the period from 1963 to 2022; C is the process of calculating the compound high-temperature and drought index by the t-copula method, is the survival cumulative probability density function of temperature, ​is the survival cumulative probability density function of precipitation; P is the calculation process of the cumulative probability density function, is the joint cumulative distribution of high-temperature events and precipitation deficit events.

[0033] Extract the indices of June, July, and August each year from the compound high-temperature drought index PI of 12 months per year from 1963 to 2022 to form the compound high-temperature drought index; the data obtained after averaging over the month dimension is denoted as the compound high-temperature drought index data PIJJA for the Northeast region in summer, and its calculation formula is as follows: ; In the formula: y is the year, t is the t-th latitude of the Northeast region, p is the p-th longitude of the Northeast region, PIJJA y，t，p is the compound high-temperature drought index in summer of year y at the grid point of the t-th latitude and p-th longitude, PI y，6，t，p is the compound high-temperature drought index in June of year y at the grid point of the t-th latitude and p-th longitude, PI y，7，t，p is the compound high-temperature drought index in July of year y at the grid point of the t-th latitude and p-th longitude, PI y，8，t，p is the compound high-temperature drought index in August of year y at the grid point of the t-th latitude and p-th longitude.

[0034] Further, the compound high-temperature drought index PIJJA for the summer in Northeast China from 1963 to 2022 is standardized to obtain the standardized compound high-temperature drought index ZPIJJA_NEC for the summer in Northeast China from 1963 to 2022, and its calculation formula is as follows: ; In the formula: y is the year, ZPIJJA_NEC is the standardized compound high-temperature drought index for the summer in Northeast China from 1963 to 2022, Z is the standardization process, PIJJA is the compound high-temperature drought index for the summer in Northeast China from 1963 to 2022, μ(PIJJA) is the average value of PIJJA from 1963 to 2022, and σ(PIJJA) is the standard deviation of PIJJA from 1963 to 2022.

[0035] Step 2: Obtain the predictor indices from 1963 to 2023, including sea ice concentration index, sea surface temperature index, and soil moisture index; Collect the monthly global sea ice concentration SIC and sea surface temperature SST data from 1963 to 2023 through the Hadley dataset, and the data horizontal resolution is 1° 1°; Collect the monthly global soil moisture SW data from 1963 to 2023 through the ERA5 dataset, and the data horizontal resolution is 1° 1°; The annual increment method is used to process the sea ice concentration SIC, sea surface temperature SST, and soil moisture SW from 1963 to 2023 to obtain the annual increment values of sea ice concentration D SIC, sea surface temperature D SST, and soil moisture D SW in the form of annual increments. The calculation formulas are as follows: ; In the formula: y is the year, m is the month, m = 1, 2, 3,..., 12, t is the t-th latitude, t = 1, 2, 3,..., 181, p is the p-th longitude, p = 1, 2, 3,..., 360; DXXX is the data of the previous meteorological factor in the form of annual increment, XXX = SIC, SST, SW, and XXX is the data of the previous meteorological factor, XXX = SIC, SST, SW; Extract the sea ice concentration data SICM for the key month of March from the monthly scale of the annual increment value of sea ice concentration D SIC. The calculation formula is as follows: ; In the formula: y is the year, t is the t-th latitude, t = 1, 2, 3,..., 181, p is the p-th longitude, p = 1, 2, 3,..., 360, and SICM y,t,p is the annual increment value of the sea ice concentration in March of year y at the grid point of the t-th latitude and the p-th longitude, and D SIC y,3,t,p is the annual increment value of the sea ice concentration in March of year y at the grid point of the t-th latitude and the p-th longitude; The key area of the Barents Sea sea ice concentration is defined as: 30°E–60°E, 72°N–78°N ( Figure 2 the black rectangular box in), based on the annual increment value of sea ice concentration SICM, extract and average the SICM where both the latitude grid point and the longitude grid point are within the key area of the Barents Sea sea ice concentration to obtain the annual increment value of the sea ice concentration in the key area of the Barents Sea SICBS. The calculation formula is as follows: ; In the formula: y is the year, n1 is the number of grid points where the longitude and latitude fall within the key area of the Barents Sea sea ice concentration, (ti, pi) is the i-th grid point where the longitude and latitude fall within the key area of the sea ice concentration, i = 1, 2, 3,..., n1, SICBS is the annual increment value of the sea ice concentration in the key area of the Barents Sea, and SICM(y, ti, pi) is the sea ice concentration value in March of year y at the i-th grid point falling within the key area of the Barents Sea sea ice concentration, i = 1, 2, 3,..., n1; Standardize the annual inter - annual increment value SICBS of sea ice concentration in the key area of the Barents Sea ice concentration to obtain the sea ice concentration index. The calculation formula is as follows: ; In the formula: y is the year, ZSICBS is the sea ice concentration index, Z is the standardization process, SICBS is the annual inter - annual increment value of sea ice concentration in the key area of the Barents Sea ice concentration, μ(SICBS) is the average value of SICBS from year Y - N - 1 to year Y, and σ(SICBS) is the standard deviation of SICBS from year Y - N - 1 to year Y.

[0036] Extract the sea surface temperature data SSTM of the key month of February from the monthly scale of the annual inter - annual increment value DSST of sea surface temperature. The calculation formula is as follows: ; In the formula: y is the year, t is the t - th latitude, t = 1, 2, 3,..., 181, p is the p - th longitude, p = 1, 2, 3,..., 360, SSTM y,t,p is the annual inter - annual increment value of sea surface temperature in February of year y at the grid point of the t - th latitude and the p - th longitude, DSST y,2,t,p is the annual inter - annual increment value of sea surface temperature in February of year y at the grid point of the t - th latitude and the p - th longitude; The tropical Indian Ocean and the tropical Pacific region are defined as: 30°E–60°W, 30°S–30°N ( Figure 3 the black rectangular box in the figure). Based on the annual inter - annual increment value SSTM of sea surface temperature, extract and average the SSTM values where both the latitude grid points and the longitude grid points are within the range of the tropical Indian Ocean and the tropical Pacific region to obtain the annual inter - annual increment value SSTBS of sea surface temperature in the tropical Indian Ocean and the tropical Pacific region. The calculation formula is as follows: ; In the formula: y is the year, n2 is the number of grid points where the longitude and latitude fall within the tropical Indian Ocean and the tropical Pacific region, (ti, pi) is the i - th grid point where the longitude and latitude fall within the tropical Indian Ocean and the tropical Pacific region, i = 1, 2, 3,..., n2, SSTBS is the annual inter - annual increment value of sea surface temperature in the tropical Indian Ocean and the tropical Pacific region, SSTM(y, ti, pi) is the sea surface temperature value in February of year y at the i - th grid point that falls within the tropical Indian Ocean and the tropical Pacific region, i = 1, 2, 3,..., n2; Standardize the annual inter - annual increment value SSTBS of sea surface temperature in the key area of sea surface temperature to obtain the sea surface temperature index. The calculation formula is as follows: ; Where: y is the year, ZSSTBS is the sea surface temperature index, Z is the standardization process, SSTBS is the interannual increment value of the sea surface temperature in the tropical Indian Ocean and the tropical Pacific Ocean, μ(SSTBS) is the average value of SSTBS from 1964 to 2023, and σ(SSTBS) is the standard deviation of SSTBS from 1964 to 2023.

[0037] Extract the soil moisture data SWM for the key month of April from the monthly scale of the interannual increment value DSW of soil moisture. The calculation formula is as follows: ; Where: y is the year, t is the t-th latitude, t = 1, 2, 3,..., 181, p is the p-th longitude, p = 1, 2, 3,..., 360, and SWM y,t,p is the interannual increment value of the soil moisture in April of year y at the grid point of the t-th latitude and the p-th longitude, and DSW y,4,t,p is the interannual increment value of the soil moisture in April of year y at the grid point of the t-th latitude and the p-th longitude; The key area of soil moisture in northwestern Siberia is defined as: 60°E–120°E, 60°N–75°N ( Figure 4 the black rectangular box in), based on the interannual increment value SWM of soil moisture, extract and average the SWM data where both the latitude grid point and the longitude grid point are within the key area of soil moisture in northwestern Siberia to obtain the interannual increment value SWBS of the soil moisture in the key area of soil moisture in northwestern Siberia. The calculation formula is as follows: ; Where: y is the year, n3 is the number of grid points where the longitude and latitude fall within the key area of soil moisture in northwestern Siberia, (ti, pi) is the i-th grid point where the longitude and latitude fall within the key area of soil moisture in northwestern Siberia, i = 1, 2, 3,..., n1, SWBS is the interannual increment value of the soil moisture in the key area of soil moisture in northwestern Siberia, and SWM(y, ti, pi) is the soil moisture value in April of year y at the i-th grid point that falls within the key area of soil moisture in northwestern Siberia, i = 1, 2, 3,..., n1; Perform standardization processing on the interannual increment value SWBS of the soil moisture in the key area of soil moisture to obtain the soil moisture index. The calculation formula is as follows: ; Where: y is the year, ZSWBS is the soil moisture index, Z is the standardization process, SWBS is the interannual increment value of the soil moisture in the key area of soil moisture in northwestern Siberia, μ(SWBS) is the average value of SWBS from 1964 to 2023, and σ(SWBS) is the standard deviation of SWBS from 1964 to 2023.

[0038] Step 3: Input the obtained prediction factor indices for 2023 into the prediction model of the prediction quantity DPI pre-constructed based on the prediction factor indices of previous years to predict the DPI prediction value for 2023; According to the standardized composite high-temperature drought index ZPIJJA_NEC in summer from 1963 to 2022 in Northeast China obtained in Step 1 above, the standardized composite high-temperature drought inter-annual increment index DPI in summer from 1964 to 2022 in the target area in the form of inter-annual increment is defined by the inter-annual increment method, and its calculation formula is as follows: ; In the formula: y is the year, DPI is the standardized composite high-temperature drought inter-annual increment index in summer from 1964 to 2022 in Northeast China, and ZPIJJA_NEC is the standardized composite high-temperature drought index in summer from 1963 to 2022 in Northeast China; Take the sea ice concentration index ZSICBS, sea surface temperature index ZSSTBS, and soil moisture index ZSWBS from 1964 to 2022 calculated in Step 2 above as prediction factor indices, and DPI as the prediction quantity. After confirming the independence between prediction factors, a prediction model of the prediction quantity DPI is constructed based on the multiple linear regression method, and the prediction equation is: ; In the formula: y is the year, DPI is the predicted value of the standardized composite high-temperature drought inter-annual increment index in summer from 1964 to 2022 in Northeast China, , , are the defined prediction factor indices.

[0039] According to the prediction factor indices ZSICBS, ZSSTBS, and ZSWBS for 2023 calculated in Step 2 above, input the prediction factor indices for 2023 into the prediction model of the prediction quantity DPI constructed based on the prediction factor indices of previous years to obtain the DPI prediction value for 2023, and its calculation formula is as follows: ; In the formula: is the predicted value of the standardized composite high-temperature drought inter-annual increment index in summer in Northeast China in 2023, , , are the prediction factor indices for 2023.

[0040] Step 4: Add the composite high-temperature drought data to the predicted DPI value for 2023 to obtain the predicted value of the composite high-temperature drought index for 2023 ; Add the predicted value of the standardized composite high-temperature drought interannual increment index for the summer of 2023 in Northeast China obtained in Step 3 above to the standardized composite high-temperature drought index for the summer of 2022 in Northeast China obtained in Step 1 above , to obtain the predicted value for the forecast year Y , and its calculation formula is as follows: ; In the formula: ZPIJJA_REGION 2023 is the predicted value of the standardized composite high-temperature drought index for the summer of 2023 in Northeast China, DPI 2023 is the predicted value of the standardized composite high-temperature drought interannual increment index for the summer of 2023 in Northeast China, and ZPIJJA_REGION 2022 is the true value of the standardized composite high-temperature drought index for the summer of 2022 in Northeast China.

[0041] The above is only the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and deformations can be made, and these improvements and deformations should also be regarded as the protection scope of the present invention.

Claims

1. A method for predicting compound high temperature and drought in a target area based on dynamics, characterized in that, The method includes: Obtain the standardized composite high temperature and drought index ZPIJJA_REGION for the summer in the target area in year Y - 1 Y-1 ; Obtaining the predictor indices for previous years and the target prediction year Y, including sea ice concentration index, sea surface temperature index, and soil moisture index; Inputting the predictor indices for the target prediction year Y obtained into the prediction model of the prediction quantity DPI pre-constructed based on the predictor indices of previous years to predict the DPI prediction value for year Y; Add the composite high temperature and drought index ZPIJJA_REGION Y-1 to the predicted DPI value for year Y to obtain the predicted value of the composite high temperature and drought index ZPIJJA_REGION for the predicted year Y Y ; where Y is the composite high-temperature and drought year of the target area to be predicted.

2. The method for predicting the composite high temperature and drought in the target area based on dynamics according to claim 1, wherein Obtain the standardized composite high temperature and drought index ZPIJJA_REGION for the target area in summer of year Y - 1 Y-1 , including: Collect monthly gridded temperature and precipitation data for the target area from Y - N years to Y - 1 year before the forecast year Y through the CN05.1 dataset, with a horizontal resolution of 0.25° 0.25°; N is the time span of data collection, in years; Based on the monthly temperature and precipitation gridded data, using the t-coupla method to construct the composite high-temperature and drought index PI characterizing the intensity of the composite high-temperature and drought in the target area during the period from Y-N years to Y-1 years based on the joint cumulative probability density function; Extracting the indices for June, July, and August of each year from the composite high-temperature and drought index PI for each of the 12 months from Y-N years to Y-1 years, and averaging over the month dimension to obtain the data PIJJA characterizing the intensity of the summer composite high-temperature and drought in the target area; Performing standardization processing on the composite high-temperature and drought data PIJJA for the summers from Y-N years to Y-1 years in the target area to obtain the standardized composite high-temperature and drought index ZPIJJA_REGION for the summers from Y-N years to Y-1 years in the target area.

3. The method for predicting compound high temperature and drought in a target area based on dynamics according to claim 2, wherein, Constructing the composite high-temperature and drought index PI characterizing the intensity of the composite high-temperature and drought in the target area during the period from Y-N years to Y-1 years includes: Construct the cumulative probability density function of temperature and precipitation , and the calculation formula of the cumulative probability density function of temperature and precipitation is as follows: ； In the formula: is the cumulative probability density function of air temperature and precipitation. For i = 1, 2, when i = 1, is the cumulative probability density function of air temperature. When i = 2, is the cumulative probability density function of precipitation; P is the calculation process of the cumulative probability density function, is the cumulative distribution of high-temperature events or precipitation shortage events. For i = 1, 2, when i = 1, is the cumulative distribution of high-temperature events. When i = 2, is the cumulative distribution of precipitation shortage events; Through the cumulative probability density functions of air temperature and precipitation Calculate the individual return periods of high-temperature events and precipitation deficit events respectively , where the calculation formulas for the individual return periods of high-temperature events and precipitation deficit events are as follows: ； In the formula: is the individual recurrence period of the high-temperature event or precipitation shortage event, i = 1, 2. When i = 1, is the individual recurrence period of the high-temperature event. When i = 2, is the individual recurrence period of the precipitation shortage event; is the calculated cumulative probability density function of temperature and precipitation; Based on the obtained cumulative probability density distribution functions of air temperature and precipitation Calculate the survival cumulative probability density distribution functions of air temperature and precipitation , and its calculation formula is as follows: ； In the formula: is the survival cumulative probability density distribution function of temperature and precipitation, i = 1, 2. When i = 1, is the survival cumulative probability density distribution function of temperature. When i = 2, is the survival cumulative probability density distribution function of precipitation; is the cumulative probability density function of temperature and precipitation, i = 1, 2. When i = 1, is the cumulative probability density function of temperature. When i = 2, is the cumulative probability density function of precipitation; Using the t-copula method, calculating the joint survival cumulative probability density function of the composite high-temperature and drought event based on the calculated survival cumulative probability density distribution functions of temperature and precipitation, and defining the joint survival cumulative probability density function as the composite high-temperature and drought index PI characterizing the intensity of the composite high-temperature and drought, and its calculation formula is as follows: ； Where: PI is the defined composite high temperature and drought index; C is the process of calculating the composite high temperature and drought index by the t-copula method, is the survival cumulative probability density function of temperature, is the survival cumulative probability density function of precipitation; P is the calculation process of the cumulative probability density function, is the joint cumulative distribution of high temperature events and precipitation shortage events.

4. The method for predicting composite high-temperature drought in a target area based on dynamics according to claim 1, wherein The prediction of the DPI prediction value for year Y includes: Calculating the standardized interannual increment index of the predictor for year Y, with the prediction year being year Y; After all the predictor indices are calculated, inputting the predictor indices for year Y into the prediction model of the prediction quantity DPI constructed based on the predictor indices of previous years to obtain the DPI prediction value for year Y, and its calculation formula is as follows: ； In the formula: Y is the forecast year, a, b, and c are regression coefficients, d is the regression constant, and DPI Y is the forecast value of the standardized composite high-temperature and drought interannual increment index in the summer of year Y in the target area, and x1 Y , x2 Y , x3 Y are the index values of each forecast factor in year Y.

5. The method for predicting the composite high temperature and drought in the target area based on dynamics according to claim 1, characterized in that, The predicted value ZPIJJA_REGION of the composite high temperature and drought index for the predicted year Y Y , including: The predicted value DPI of the standardized composite high-temperature and drought interannual increment index for the target region in the summer of year Y Y is added to the standardized composite high-temperature and drought index ZPIJJA_REGION for the target region in the summer of year Y-1 Y-1 to obtain the predicted value ZPIJJA_REGION for the forecast year Y Y The calculation formula is as follows: ； Where: Y is the forecast year, and ZPIJJA_REGION Y is the forecast value of the standardized composite high temperature and drought index for the summer of year Y in the target region, and DPI Y is the forecast value of the standardized composite high temperature and drought inter-annual increment index for the summer of year Y in the target region, and ZPIJJA_REGION Y-1 is the true value of the standardized composite high temperature and drought index for the summer of year Y - 1 in the target region.

6. The method for predicting compound high temperature and drought in a target area based on dynamics according to claim 4, wherein, The construction method of the prediction model of the prediction quantity DPI includes: Based on the standardized composite high-temperature and drought index ZPIJJA_REGION for the summers from Y-N years to Y-1 years in the target area, using the interannual increment method to define the standardized composite high-temperature and drought interannual increment index DPI for the summers from Y-N-1 years to Y-1 years in the target area in the form of interannual increment, and its calculation formula is as follows: ； In the formula: y is the year, DPI is the standardized composite high-temperature and drought interannual increment index for the summers from Y-N-1 years to Y-1 years in the target area, and ZPIJJA_REGION is the standardized composite high-temperature and drought index for the summers from Y-N years to Y-1 years in the target area; Taking the sea ice concentration index, sea surface temperature index, and soil moisture index from Y-N-1 years to Y-1 years in previous years as the predictor indices, and DPI as the prediction quantity, after confirming the independence between the predictors, constructing the prediction model of the prediction quantity DPI based on the multiple linear regression method, where the prediction equation is: ； Where: y is the year, a, b, and c are regression coefficients, d is the regression constant, DPI is the predicted value of the standardized composite high-temperature and drought interannual increment index for the summer from year Y - N - 1 to year Y - 1 in the target area, and x1, x2, and x3 are the defined predictor factor indices.

7. The method for predicting compound high temperature and drought in a target area based on dynamics according to claim 1, characterized in that The method for obtaining the predictor factor indices for previous years and the predicted year Y includes: Collect monthly sea ice concentration (SIC) and sea surface temperature (SST) data for the global from year Y-N to year Y through the Hadley dataset, with a horizontal resolution of 1° 1°; Collect monthly soil moisture SW data globally from year Y - N to year Y through the ERA5 dataset, with a horizontal resolution of 1° 1°; N is the time span of data collection, in years; Using the interannual increment method to process the sea ice concentration SIC, sea surface temperature SST, and soil moisture SW from year Y - N to year Y to obtain the interannual increment values of sea ice concentration D SIC, sea surface temperature D SST, and soil moisture D SW in the form of interannual increments. The calculation formulas are as follows: ； Where: y is the year, m is the month, m = 1, 2, 3,..., 12, t is the t-th latitude, t = 1, 2, 3,..., 181, p is the p-th longitude, p = 1, 2, 3,..., 360; DXXX is the data of the previous meteorological factor in the form of interannual increment, DXXX = D SIC, D SST, D SW, XXX is the data of the previous meteorological factor, XXX = SIC, SST, SW; Based on the interannual increment values of sea ice concentration D SIC, sea surface temperature D SST, and soil moisture D SW in the form of interannual increments, the sea ice concentration index, sea surface temperature index, and soil moisture index are calculated.

8. The method for predicting composite high temperature and drought in a target area based on dynamics according to claim 7, wherein, The method for obtaining the sea ice concentration index includes: Extract the key months of sea ice from the monthly scale of the interannual increment value of sea ice concentration DSIC. The sea ice concentration data SICM is calculated as follows: ； Where: y is the year, t is the t-th latitude, t = 1, 2, 3,..., 181, p is the p-th longitude, p = 1, 2, 3,..., 360, and SICM(y, t, p) is the interannual increment value of sea ice concentration at the grid point of the t-th latitude and the p-th longitude in year y for the month of , DSIC(y, for the month of = 1, 2, 3,..., 12; Based on the interannual increment value of sea ice concentration SICM, extract and average the SICM where both the latitude grid point and the longitude grid point are within the sea ice concentration key area to obtain the interannual increment value of sea ice concentration SICBS in the sea ice concentration key area. The calculation formula is as follows: ; Where: y is the year, n1 is the number of grid points where the longitude and latitude fall within the key area of sea ice concentration, (ti, pi) is the i-th grid point where the longitude and latitude fall within the key area of sea ice concentration, i = 1, 2, 3,..., n1, SICBS is the interannual increment value of sea ice concentration in the key area of sea ice concentration, and SICM(y, ti, pi) is the sea ice concentration value in the y-th year for the i-th grid point that falls within the key area of sea ice concentration monthly sea ice concentration value; Perform standardization processing on the interannual increment value of sea ice concentration SICBS in the sea ice concentration key area to obtain the sea ice concentration index. The calculation formula is as follows: ; Where: y is the year, Z SICBS is the sea ice concentration index, Z is the standardization process, SICBS is the interannual increment value of sea ice concentration in the sea ice concentration key area, μ(SICBS) is the average value of SICBS from year (Y - N - 1) to year Y, and σ(SICBS) is the standard deviation of SICBS from year (Y - N - 1) to year Y.

9. The method for predicting compound high temperature and drought in a target area based on dynamics according to claim 7, characterized in that The method for obtaining the sea surface temperature index includes: Extract the key months of sea surface temperature from the monthly scale of the interannual increment value of sea surface temperature DSST The sea surface temperature data SSTM, and its calculation formula is as follows: ; where: y is the year, t is the t-th latitude, t = 1, 2, 3,..., 181, p is the p-th longitude, p = 1, 2, 3,..., 360, and SSTM(y, t, p) is the interannual increment value of sea surface temperature at the grid point of the t-th latitude and the p-th longitude in year y for the month of , DSST(y, is the interannual increment value of sea surface temperature at the grid point of the t-th latitude and the p-th longitude in year y = 1, 2, 3,..., 12; Based on the interannual increment value of sea surface temperature SSTM, extract and average the SSTM where both the latitude grid point and the longitude grid point are within the sea surface temperature key area to obtain the interannual increment value of sea surface temperature SSTBS in the sea surface temperature key area. The calculation formula is as follows: ; Where: y is the year, n2 is the number of grid points where the longitude and latitude fall within the key sea surface temperature region, (ti, pi) is the i-th grid point where the longitude and latitude fall within the key sea surface temperature region, i = 1, 2, 3,..., n2, SSTBS is the interannual increment value of the sea surface temperature in the key sea surface temperature region, and SSTM(y, ti, pi) is the sea surface temperature value in the y-th year for the i-th grid point that falls within the key sea surface temperature region month sea surface temperature value; Perform standardization processing on the interannual increment value of sea surface temperature SSTBS in the sea surface temperature key area to obtain the sea surface temperature index. The calculation formula is as follows: ； Where: y is the year, Z SSTBS is the sea surface temperature index, Z is the standardization process, SSTBS is the interannual increment value of sea surface temperature in the sea surface temperature key area, μ(SSTBS) is the average value of SSTBS from year (Y - N - 1) to year Y, and σ(SSTBS) is the standard deviation of SSTBS from year (Y - N - 1) to year Y.

10. The method for predicting the composite high-temperature drought in the target area based on dynamics according to claim 7, characterized in that, The method for obtaining the soil moisture index includes: Extract the key months of soil moisture from the monthly scale of the interannual increment value DSW of soil moisture The soil moisture data SWM, and its calculation formula is as follows: ； Where: y is the year, t is the t-th latitude, t = 1, 2, 3,..., 181, p is the p-th longitude, p = 1, 2, 3,..., 360, and SWM(y, t, p) is the interannual increment value of soil moisture at the grid point of the t-th latitude and the p-th longitude in year y for the month of, DSW(y, , t, p) is the interannual increment value of soil moisture at the grid point of the t-th latitude and the p-th longitude in year y for the month of, = 1, 2, 3,..., 12; Based on the annual inter - annual increment value of soil moisture SWM, the SWM indices where both the latitude grid points and longitude grid points are within the soil moisture key area are extracted and averaged to obtain the annual inter - annual increment value of soil moisture SWBS in the soil moisture key area. Its calculation formula is as follows: ； Where: y is the year, n3 is the number of grid points where the longitude and latitude fall within the critical soil moisture area, (ti, pi) is the i-th grid point where the longitude and latitude fall within the critical soil moisture area, i = 1, 2, 3,..., n1, SWBS is the interannual increment value of soil moisture in the critical soil moisture area, and SWM(y, ti, pi) is the soil moisture value of the i-th grid point falling within the critical soil moisture area in year y month soil moisture value; The annual inter - annual increment value of soil moisture SWBS in the soil moisture key area is standardized to obtain the soil moisture index. Its calculation formula is as follows: ； In the formula: y is the year, ZSWBS is the soil moisture index, Z is the standardization process, SWBS is the annual inter - annual increment value of soil moisture in the soil moisture key area, μ(SWBS) is the average value of SWBS from year Y - N - 1 to year Y, and σSWBS is the standard deviation of SWBS from year (Y - N - 1) to year Y.

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