Kinetic-based target area compound high temperature drought prediction method

Abstract

The application discloses a target area compound high-temperature drought prediction method based on kinetics and belongs to the technical field of meteorological disaster prediction. The method comprises the following steps: obtaining a standardized compound high-temperature drought index of a target area in summer of previous years, and obtaining a prediction factor index of the previous years and a prediction year Y; inputting the prediction factor index of the prediction year Y into a prediction model of a prediction quantity DPI which is constructed in advance based on the prediction factor index of the previous years, and predicting a DPI prediction value of the year Y; adding the compound high-temperature drought index of the year Y-1 and the DPI prediction value of the year Y to obtain a compound high-temperature drought index prediction value of the prediction year Y; the method can predict the compound high-temperature drought condition of the target area at least one month in advance by taking the monthly scale early prediction factor index as a prediction factor and incorporating the prediction factor into the compound high-temperature drought prediction model, and the prediction timeliness is greatly improved while the prediction effect is ensured.

Description

Technical Field

[0001] This invention relates to a dynamic-based method for predicting composite high-temperature drought in target areas, belonging to the field of meteorological disaster prediction technology. Background Technology

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

[0003] Against the backdrop of global warming, frequent high-temperature droughts have had a profound impact on human society and natural ecosystems. In agricultural production, high temperatures and droughts not only hinder crop growth and development, significantly reducing yield and quality, but also severely affect the normal production of cash crops such as tea and tobacco. Regarding water resources, high temperatures accelerate surface water evaporation, leading to a continuous decline in groundwater levels, while water quality deteriorates due to increased pollutant concentrations, further exacerbating water scarcity and pollution problems. Furthermore, high temperatures and droughts disrupt ecosystem balance, reduce biodiversity, and provide opportunities for invasive alien species. For human society, high temperatures and droughts not only restrict daily life and affect industrial production, but also increase health risks. To address the challenges posed by high-temperature and drought events, a large number of studies have focused on the changing characteristics, physical mechanisms, and predictive models of high temperatures and droughts.

[0004] Existing technologies use the interannual increment method to predict summer temperatures. This method calculates the temperature increment between adjacent years and uses these increments as forecasts to predict temperature changes in future years. Existing technologies use two numerical models, WRF (Weather Research and Forecasting Model) and CCSM4 (Community Climate System Model version 4), to predict summer precipitation. Existing technologies predict the frequency of extreme summer precipitation based on changes in South Indian Sea temperature and sea ice. Currently, most of the approved prediction models for high temperature and drought focus on single events. For example, Chinese patent application CN112330075A discloses a method for predicting seasonal temperatures in China based on the synergistic changes of the East Asian subtropical jet stream and the polar front jet stream; Chinese patent application CN118503655A discloses a method and system for predicting drought under changing environments based on an improved ensemble prediction method. Furthermore, some patents use meteorological elements with a time range close to the prediction period as predictive factors to construct models. For example, Chinese patent application CN118962847A discloses a drought prediction method for plateau agriculture, which predicts the degree of drought in plateau agricultural areas based on precipitation, temperature, air humidity, and wind intensity. Meanwhile, models utilizing artificial intelligence technology for prediction are also increasingly common. For instance, 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 networks.

[0005] However, most existing models focus on predicting drought using a single variable like low precipitation, or only predict single events like high temperatures or drought, lacking the ability to predict complex high-temperature and drought events. Using meteorological elements close to the forecast period as predictive factors in model construction has shortcomings in terms of prediction timeliness. While AI-based prediction models have advantages such as fast prediction speed and strong big data processing capabilities, their reliability needs further verification due to the lack of incorporation of relevant physical mechanisms. Dynamic statistical models are applied in various climate prediction models; specifically, they refer to statistical models based on an understanding of physical mechanisms.

[0006] Some of the current high-temperature drought prediction models focus only on a single variable for prediction, failing to cover the complexity of complex high-temperature drought events and lacking comprehensive predictive capabilities for such events. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art and provide a dynamic-based method for predicting composite high-temperature drought in target areas.

[0008] To achieve the above objectives / to solve the above technical problems, the present invention is implemented using the following technical solution: A dynamic-based method for predicting combined high-temperature drought in target areas, the method comprising: Obtain the standardized composite high temperature and drought index ZPIJJA_REGION for the target region in summer of year Y-1. Y-1 ; Obtain forecast factor indices from previous years and the forecast year Y, including sea ice concentration index, sea surface temperature index, and soil moisture index; The forecast factor index of the year Y to be predicted is input into the forecast model of the forecast quantity DPI pre-built based on the forecast factor index of previous years to predict the DPI forecast value of year Y. The composite high temperature and drought index ZPIJJA_REGION Y-1 Adding this to the predicted DPI value for year Y yields the predicted composite high temperature and drought index value ZPIJJA_REGION for year Y. Y ; Where Y represents the combined high temperature and drought year in the target region to be predicted.

[0009] Optionally, obtain the standardized composite high temperature and drought index ZPIJJA_REGION for the target region in summer of year Y-1. Y-1 ,include: Monthly gridded temperature and precipitation data for the target region from YN to Y-1, preceding the forecast year Y, were collected using the CN05.1 dataset. The horizontal resolution of the data is 0.25°. 0.25°; N represents the time span of data collection, in years; Based on the gridded monthly temperature and precipitation data, and using the t-coupla method, a composite high-temperature drought index PI is constructed to characterize the intensity of composite high-temperature drought in the target area from YN to Y-1, based on the joint cumulative probability density function. The composite high temperature and drought index PI for each of the 12 months from year YN to year Y-1 was extracted to obtain the index for June, July and August of each year. The index was averaged over the monthly dimension to obtain the data PIJJA representing the intensity of summer composite high temperature and drought in the target area. After standardizing the composite high temperature and drought data PIJJA of the target area from summer of year YN to summer of year Y-1, the standardized composite high temperature and drought index ZPIJJA_REGION of the target area from summer of year YN to summer of year Y-1 is obtained.

[0010] Optionally, a composite high-temperature drought index PI is constructed to characterize the intensity of combined high-temperature drought in the target region from year YN to year Y-1, including: Construct the cumulative probability density function of temperature and precipitation The cumulative probability density function formulas for temperature and precipitation are as follows: ; In the formula: Let be the cumulative probability density function of temperature and precipitation, i=1,2, where when i=1, Let be the cumulative probability density function of temperature. When i=2, Let be the cumulative probability density function of precipitation; P represents the calculation process of the cumulative probability density function. Let i be the cumulative distribution of high temperature events or insufficient precipitation events, i=1,2, where when i=1, ... For the cumulative distribution of high-temperature events, when i=2, The cumulative distribution of insufficient precipitation events; Through the cumulative probability density function of temperature and precipitation Calculate the individual regression periods for high-temperature events and insufficient precipitation events separately. The formulas for calculating the individual regression periods of high-temperature events and insufficient precipitation events are as follows: ; In the formula: For high temperature events or insufficient precipitation events, the individual regression period is i=1,2, where when i=1, For the individual regression period of a high-temperature event, when i=2, The individual regression period for insufficient precipitation events; The cumulative probability density functions for calculated temperature and precipitation; Based on the obtained cumulative probability density distribution functions of temperature and precipitation The survival cumulative probability density distribution functions of temperature and precipitation were calculated. The calculation formula is as follows: ; In the formula: Let be the cumulative probability density distribution function for survival of temperature and precipitation, i=1,2, where when i=1, Let be the survival cumulative probability density distribution function of temperature. When i=2, Let be the cumulative probability density distribution function for precipitation. Let be the cumulative probability density function of temperature and precipitation, i=1,2, where when i=1, Let be the cumulative probability density function of temperature. When i=2, Let be the cumulative probability density function of precipitation; Using the t-copula method, the joint cumulative probability density function of the complex high-temperature drought event is calculated based on the calculated cumulative probability density distribution functions of air temperature and precipitation. This joint cumulative probability density function is then defined as the complex high-temperature drought index PI, characterizing the intensity of the complex high-temperature drought. The calculation formula is as follows: ; In the formula: PI is the defined composite high temperature and drought index; C is the process of calculating the composite high temperature and drought index using the t-copula method. Let be the cumulative probability density function for survival based on temperature. Let be the cumulative probability density function for precipitation; P represents the calculation process for the cumulative probability density function. This represents the combined cumulative distribution of high-temperature events and insufficient precipitation events.

[0011] Optionally, the predicted DPI forecast for year Y includes: The interannual increment index of the standardized forecast factor for year Y is calculated, and the forecast year is year Y; After all forecast factor indices have been calculated, the forecast factor indices for year Y are input into the forecast model for the forecast quantity DPI built based on the forecast factor indices 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, and DPI is the regression coefficient. Y x1 represents the predicted value of the standardized interannual increment index of high temperature and drought in the target region during the summer of year Y. Y x2 Y x3 Y These are the various forecast factor indices for year Y.

[0012] Optionally, the predicted value of the composite high temperature and drought index ZPIJJA_REGION for the forecast year Y is obtained. Y ,include: The predicted standardized interannual increment index (DPI) of the composite high temperature and drought in the target region during the summer of year Y is used. Y Add the standardized composite high temperature and drought index ZPIJJA_REGION for the summer of year Y-1 in the target region Y-1 The predicted value ZPIJJA_REGION for year Y is obtained. Y The calculation formula is as follows: ; In the formula: Y is the predicted year, ZPIJJA_REGION Y DPI is the standardized composite high temperature and drought index forecast value for the target region in summer of year Y. YZPIJJA_REGION is the predicted value of the standardized interannual increment index of high temperature and drought in the summer of year Y for the target region. Y-1 The value of the standardized composite high temperature and drought index for the target region in the summer of year Y-1 is given.

[0013] Optionally, the method for constructing the forecast model for the forecast quantity DPI includes: Based on the standardized compound high temperature and drought index ZPIJJA_REGION for the summer of YN to Y-1 in the target region, the standardized compound high temperature and drought interannual increment index DPI for the summer of YN-1 to Y-1 in the target region is defined using the interannual increment method. The calculation formula is as follows: ; In the formula: y is the year, DPI is the standardized compound high temperature and drought interannual increment index of the target region from year YN-1 to summer of year Y-1, and ZPIJJA_REGION is the standardized compound high temperature and drought index of the target region from year YN to summer of year Y-1. Using the sea ice concentration index, sea surface temperature index, and soil moisture index from previous years YN-1 to Y-1 as forecasting factors, and DPI as the forecast quantity, after confirming the independence among the forecasting factors, a forecasting model for the forecast quantity DPI is constructed based on the multiple linear regression method, where the forecasting 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 interannual increment index for the summer of the target region from year YN-1 to year Y-1, and x1, x2, and x3 are the defined forecast factor indices.

[0014] Optionally, methods for obtaining the forecast factor index for previous years and the year Y to be forecasted include: Monthly sea ice concentration (SIC) and sea surface temperature (SST) data were collected globally from year YN to year Y using the Hadley dataset, with a horizontal resolution of 1°. 1°; Monthly soil moisture (SW) data from YN to Y were collected globally using the ERA5 dataset, with a horizontal resolution of 1°. 1°; N represents the time span of data collection, in years; The interannual increments of sea ice concentration (SIC), sea surface temperature (SST), and soil moisture (SW) from YN to Y were processed using the interannual increment method to obtain the interannual increment values ​​of sea ice concentration (DSIC), sea surface temperature (DSST), and soil moisture (DSW) in the form of interannual 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 previous meteorological factor data in the form of interannual increment, DXXX=DSIC, DSST, DSW, XXX is the previous meteorological factor data, 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 increments, 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 key months for sea ice from the monthly scale of the annual sea ice concentration increment DSIC. The calculation formula for the SICM sea ice concentration data 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 year at the grid point of the t-th latitude and p-th longitude. The interannual increase in sea ice concentration for each month, DSIC(y, (t, p) represents the year y at the grid point with latitude t and longitude p. The annual increase in sea ice concentration in the month, =1,2,3,...,12; Based on the interannual sea ice concentration increment (SICM) value, the SICM values ​​of latitude and longitude grid points simultaneously located within the critical sea ice concentration region are extracted and averaged to obtain the interannual sea ice concentration increment (SICBS) value for the critical sea ice concentration region. The calculation formula is as follows: ; In the formula: y represents the year, n1 represents the number of grid points whose latitude and longitude fall within the critical sea ice concentration zone, (ti,pi) represents the i-th grid point whose latitude and longitude fall within the critical sea ice concentration zone, i=1,2,3,...,n1, SICBS represents the interannual increment of sea ice concentration in the critical sea ice concentration zone, and SICM(y,ti,pi) represents the i-th year whose latitude and longitude fall within the critical sea ice concentration zone. Monthly sea ice concentration values; The SICBS (Sea Ice Concentration Index) values ​​of the interannual increase in sea ice concentration in key sea ice concentration areas are standardized to obtain the sea ice concentration index, which is calculated using the following formula: ; In the formula: y is the year, ZSICBS is the sea ice concentration index, Z is the standardization process, SICBS is the interannual increment of sea ice concentration in the key sea ice concentration area, μ(SICBS) is the average value of SICBS from year (YN-1) to year Y, and σ(SICBS) is the standard deviation of SICBS from year (YN-1) to year Y.

[0016] Optionally, the method for obtaining the sea surface temperature index includes: Extracting key months of sea surface temperature (DSST) on a monthly scale from the annual sea surface temperature increment. The calculation formula for the SSTM sea surface temperature data 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 SSTM(y,t,p) represents the year y at the grid point of the t-th latitude and p-th longitude. The interannual increase in sea surface temperature in a given month, DSST(y, (t, p) represents the year y at the grid point with latitude t and longitude p. The annual increase in sea surface temperature in the month, =1,2,3,...,12; Based on the interannual sea surface temperature increment (SSTM) value, the SSTM values ​​of latitude and longitude grid points simultaneously located within the key sea surface temperature region are extracted and averaged to obtain the interannual sea surface temperature increment (SSTBS) value of the key sea surface temperature region. The calculation formula is as follows: ; In the formula: y represents the year, n2 represents the number of grid points with latitude and longitude falling within the critical sea surface temperature zone, (ti,pi) represents the i-th grid point with latitude and longitude falling within the critical sea surface temperature zone, i=1,2,3,...,n2, SSTBS represents the interannual sea surface temperature increment value of the critical sea surface temperature zone, and SSTM(y,ti,pi) represents the ith year with latitude and longitude falling within the critical sea surface temperature zone. Monthly sea surface temperature; The sea surface temperature index (SSTBS) of key sea surface temperature zones is standardized to obtain the SSTBS value. 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 increase of sea surface temperature in the key sea surface temperature zone, μ(SSTBS) is the average value of SSTBS from year (YN-1) to year Y, and σ(SSTBS) is the standard deviation of SSTBS from year (YN-1) to year Y.

[0017] Optionally, the method for obtaining the soil moisture index includes: Key months for soil moisture extraction from the monthly scale of the annual increment of soil moisture (DSW). The soil moisture data (SWM) is calculated using the following formula: ; 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 SWM(y,t,p) is the year at the grid point of the t-th latitude and p-th longitude. The interannual increase in soil moisture in a given month, DSW(y, (t, p) represents the year y at the grid point with latitude t and longitude p. The annual increase in soil moisture in each month =1,2,3,...,12; Based on the interannual increment of soil moisture (SWM), the SWM index of latitudinal and longitude grid points simultaneously located within the critical soil moisture zone is extracted and averaged to obtain the interannual increment of soil moisture (SWBS) in the critical soil moisture zone. The calculation formula is as follows: ; In the formula: y represents the year, n3 represents the number of grid points whose latitude and longitude fall within the critical soil moisture zone, (ti,pi) represents the i-th grid point whose latitude and longitude fall within the critical soil moisture zone, i=1,2,3,...,n1, SWBS represents the interannual increment of soil moisture in the critical soil moisture zone, and SWM(y,ti,pi) represents the ith year whose y-th point falls within the critical soil moisture zone. Monthly soil moisture value; The interannual increment of soil moisture in key soil moisture zones was standardized using the Soil Moisture Baseline (SWBS) to obtain the Soil Moisture Index, which is calculated using the following formula: ; In the formula: y is the year, ZSWBS is the soil moisture index, Z is the standardization process, SWBS is the interannual increment of soil moisture in the key soil moisture zone, μ(SWBS) is the average value of SWBS from year YN-1 to year Y, and σSWBS is the standard deviation of SWBS from year (YN-1) to year Y.

[0018] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: This invention uses a combined survival cumulative probability density function and the t-coupla method to calculate and define monthly gridded composite high-temperature drought index (PI) data using monthly temperature and precipitation data. The interannual incremental composite high-temperature drought index (DPI) is used as a forecast quantity, and previous meteorological factors influencing composite high-temperature drought conditions are selected as forecast factors to predict composite high-temperature drought events. This invention incorporates monthly-scale previous forecast factor indices into the composite high-temperature drought forecast model, enabling it to forecast the composite high-temperature drought conditions of the target area at least one month in advance, significantly improving forecast timeliness while maintaining forecast accuracy. Attached Figure Description

[0019] Figure 1 This is a diagram illustrating the target area range in an embodiment of the present invention; Figure 2 This is a diagram illustrating the key area range for defining the Barents Sea ice index in this embodiment of the invention. Figure 3 This is a diagram illustrating the key regional range of the La Niña-like sea surface temperature index defined in this embodiment of the invention; Figure 4 This is a diagram illustrating the key area range for soil moisture index definition in northwestern Siberia in this embodiment of the invention. Figure 5 This is a graph showing the correlation coefficients between (a) DPI and (b) ZPINEC predicted values ​​(dashed lines) and actual values ​​(solid lines) of the composite high temperature and drought index forecast model for the target area in 2019, constructed using data from 1961 to 2018 in an embodiment of the present invention. Figure 6 This is a schematic diagram of the process for predicting the combined high temperature and drought in Northeast China in an embodiment of the present invention; Figure 7 This is a schematic diagram of the process for predicting the combined high temperature and drought in the target area in this invention. Detailed Implementation

[0020] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0021] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are used only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0022] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0023] like Figures 1-7 As shown, a dynamic-based method for predicting combined high-temperature drought in target areas is disclosed, the method comprising: A dynamic-based method for predicting combined high-temperature drought in target areas, the method comprising: Step 1: Obtain the standardized composite high temperature and drought index ZPIJJA_REGION for the target region in summer of year Y-1. Y-1 ; Step 2: Obtain the forecast factor indices for previous years and the year Y to be forecasted, including sea ice concentration index, sea surface temperature index and soil moisture index; Step 3: Input the obtained forecast factor index for the year Y to be predicted into the forecast model based on the forecast quantity DPI pre-built on the forecast factor index of previous years, and predict the DPI forecast value for year Y. Step 4, add the composite high temperature and drought index ZPIJJA_REGION Y-1 Adding this to the predicted DPI value for year Y yields the predicted composite high temperature and drought index value ZPIJJA_REGION for year Y. Y ; Where Y represents the combined high temperature and drought year in the target region to be predicted.

[0024] Step 1 involves obtaining the standardized composite high temperature and drought index ZPIJJA_REGION for the target region in summer of year Y-1. Y-1 ,include: Monthly gridded temperature and precipitation data for the target region from YN to Y-1, preceding the forecast year Y, were collected using the CN05.1 dataset. The horizontal resolution of the data is 0.25°. 0.25°; N represents the time span of data collection, in years; Based on the gridded monthly temperature and precipitation data, and using the t-coupla method, a composite high-temperature drought index PI is constructed to characterize the intensity of composite high-temperature drought in the target area from YN to Y-1, based on the joint cumulative probability density function. The composite high temperature and drought index PI for each of the 12 months from year YN to year Y-1 was extracted to obtain the index for June, July and August of each year. The index was averaged over the monthly dimension to obtain the data PIJJA representing the intensity of summer composite high temperature and drought in the target area. After standardizing the composite high temperature and drought data PIJJA of the target area from summer of year YN to summer of year Y-1, the standardized composite high temperature and drought index ZPIJJA_REGION of the target area from summer of year YN to summer of year Y-1 is obtained.

[0025] The specific method for model construction in step 3 is as follows: Construct the composite high-temperature drought index PI, representing the intensity of combined high-temperature drought in the target region from year YN to year Y-1, including: Construct the cumulative probability density function of temperature and precipitation The cumulative probability density function formulas for temperature and precipitation are as follows: ; In the formula: Let be the cumulative probability density function of temperature and precipitation, i=1,2, where when i=1, Let be the cumulative probability density function of temperature. When i=2, Let be the cumulative probability density function of precipitation; P represents the calculation process of the cumulative probability density function. Let i be the cumulative distribution of high temperature events or insufficient precipitation events, i=1,2, where when i=1, ... For the cumulative distribution of high-temperature events, when i=2, The cumulative distribution of insufficient precipitation events; Through the cumulative probability density function of temperature and precipitation Calculate the individual regression periods for high-temperature events and insufficient precipitation events separately. The formulas for calculating the individual regression periods of high-temperature events and insufficient precipitation events are as follows: ; In the formula: For high temperature events or insufficient precipitation events, the individual regression period is i=1,2, where when i=1, For the individual regression period of a high-temperature event, when i=2, The individual regression period for insufficient precipitation events; The cumulative probability density functions for calculated temperature and precipitation; Based on the obtained cumulative probability density distribution functions of temperature and precipitation The survival cumulative probability density distribution functions of temperature and precipitation were calculated. The calculation formula is as follows: ; In the formula: Let be the cumulative probability density distribution function for survival of temperature and precipitation, i=1,2, where when i=1, Let be the survival cumulative probability density distribution function of temperature. When i=2, Let be the cumulative probability density distribution function for precipitation. Let be the cumulative probability density function of temperature and precipitation, i=1,2, where when i=1, Let be the cumulative probability density function of temperature. When i=2, Let be the cumulative probability density function of precipitation; Using the t-copula method, the joint cumulative probability density function of the complex high-temperature drought event is calculated based on the calculated cumulative probability density distribution functions of air temperature and precipitation. This joint cumulative probability density function is then defined as the complex high-temperature drought index PI, characterizing the intensity of the complex high-temperature drought. The calculation formula is as follows: ; In the formula: PI is the defined composite high temperature and drought index; C is the process of calculating the composite high temperature and drought index using the t-copula method. Let be the cumulative probability density function for survival based on temperature. Let be the cumulative probability density function for precipitation; P represents the calculation process for the cumulative probability density function. This represents the combined cumulative distribution of high-temperature events and insufficient precipitation events.

[0026] The specific method for predicting the DPI forecast value in step 3 includes: predicting the DPI forecast value for year Y, including: The interannual increment index of the standardized forecast factor for year Y is calculated, and the forecast year is year Y; After all forecast factor indices have been calculated, the forecast factor indices for year Y are input into the forecast model for the forecast quantity DPI built based on the forecast factor indices 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, and DPI is the regression coefficient. Y x1 represents the predicted value of the standardized interannual increment index of high temperature and drought in the target region during the summer of year Y. Y x2 Y x3 Y These are the various forecast factor indices for year Y.

[0027] Step 4 specifically includes: obtaining the predicted value of the composite high temperature and drought index ZPIJJA_REGION for the forecast year Y. Y ,include: The predicted standardized interannual increment index (DPI) of the composite high temperature and drought in the target region during the summer of year Y is used. Y Add the standardized composite high temperature and drought index ZPIJJA_REGION for the summer of year Y-1 in the target region Y-1 The predicted value ZPIJJA_REGION for year Y is obtained. Y The calculation formula is as follows: ; In the formula: Y is the predicted year, ZPIJJA_REGION Y DPI is the standardized composite high temperature and drought index forecast value for the target region in summer of year Y. Y ZPIJJA_REGION is the predicted value of the standardized interannual increment index of high temperature and drought in the summer of year Y for the target region. Y-1 The value of the standardized composite high temperature and drought index for the target region in the summer of year Y-1 is given.

[0028] The methods for constructing forecast models for the forecast quantity DPI include: Based on the standardized compound high temperature and drought index ZPIJJA_REGION for the summer of YN to Y-1 in the target region, the standardized compound high temperature and drought interannual increment index DPI for the summer of YN-1 to Y-1 in the target region is defined using the interannual increment method. The calculation formula is as follows: ; In the formula: y is the year, DPI is the standardized compound high temperature and drought interannual increment index of the target region from year YN-1 to summer of year Y-1, and ZPIJJA_REGION is the standardized compound high temperature and drought index of the target region from year YN to summer of year Y-1. Using the sea ice concentration index, sea surface temperature index, and soil moisture index from previous years YN-1 to Y-1 as forecasting factors, and DPI as the forecast quantity, after confirming the independence among the forecasting factors, a forecasting model for the forecast quantity DPI is constructed based on the multiple linear regression method, where the forecasting 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 interannual increment index for the summer of the target region from year YN-1 to year Y-1, and x1, x2, and x3 are the defined forecast factor indices.

[0029] Specifically, step 2 involves obtaining the forecast factor index for previous years and the year Y to be forecasted, including the following methods: Monthly sea ice concentration (SIC) and sea surface temperature (SST) data were collected globally from year YN to year Y using the Hadley dataset, with a horizontal resolution of 1°. 1°; Monthly soil moisture (SW) data from YN to Y were collected globally using the ERA5 dataset, with a horizontal resolution of 1°. 1°; N represents the time span of data collection, in years; The interannual increments of sea ice concentration (SIC), sea surface temperature (SST), and soil moisture (SW) from YN to Y were processed using the interannual increment method to obtain the interannual increment values ​​of sea ice concentration (DSIC), sea surface temperature (DSST), and soil moisture (DSW) in the form of interannual 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 previous meteorological factor data in the form of interannual increment, DXXX=DSIC, DSST, DSW, XXX is the previous meteorological factor data, 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 increments, the sea ice concentration index, sea surface temperature index, and soil moisture index are calculated. The method for obtaining the sea ice concentration index includes: Extracting key months from the monthly scale of the interannual sea ice concentration increment (DSIC). The calculation formula for the SICM sea ice concentration data 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 year at the grid point of the t-th latitude and p-th longitude. The interannual increase in sea ice concentration for each month, DSIC(y, (t, p) represents the year y at the grid point with latitude t and longitude p. The annual increase in sea ice concentration in the month, =1,2,3,...,12; Based on the interannual sea ice concentration increment (SICM) value, the SICM values ​​of latitude and longitude grid points simultaneously located within the critical sea ice concentration region are extracted and averaged to obtain the interannual sea ice concentration increment (SICBS) value for the critical sea ice concentration region. The calculation formula is as follows: ; In the formula: y represents the year, n1 represents the number of grid points whose latitude and longitude fall within the critical sea ice concentration zone, (ti,pi) represents the i-th grid point whose latitude and longitude fall within the critical sea ice concentration zone, i=1,2,3,...,n1, SICBS represents the interannual increment of sea ice concentration in the critical sea ice concentration zone, and SICM(y,ti,pi) represents the i-th year whose latitude and longitude fall within the critical sea ice concentration zone. Monthly sea ice concentration values; The SICBS (Sea Ice Concentration Index) values ​​of the interannual increase in sea ice concentration in key sea ice concentration areas are standardized to obtain the sea ice concentration index, which is calculated using the following formula: ; In the formula: y is the year, ZSICBS is the sea ice concentration index, Z is the standardization process, SICBS is the interannual increment of sea ice concentration in the key sea ice concentration area, μ(SICBS) is the average value of SICBS from year (YN-1) to year Y, and σ(SICBS) is the standard deviation of SICBS from year (YN-1) to year Y.

[0030] The method for obtaining the sea surface temperature index includes: Extracting key months of sea surface temperature (DSST) on a monthly scale from the annual sea surface temperature increment. The calculation formula for the SSTM sea surface temperature data 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 SSTM(y,t,p) represents the year y at the grid point of the t-th latitude and p-th longitude. The interannual increase in sea surface temperature in a given month, DSST(y, (t, p) represents the year y at the grid point with latitude t and longitude p. The annual increase in sea surface temperature in the month, =1,2,3,...,12; Based on the interannual sea surface temperature increment (SSTM) value, the SSTM values ​​of latitude and longitude grid points simultaneously located within the key sea surface temperature region are extracted and averaged to obtain the interannual sea surface temperature increment (SSTBS) value of the key sea surface temperature region. The calculation formula is as follows: ; In the formula: y represents the year, n2 represents the number of grid points with latitude and longitude falling within the critical sea surface temperature zone, (ti,pi) represents the i-th grid point with latitude and longitude falling within the critical sea surface temperature zone, i=1,2,3,...,n2, SSTBS represents the interannual sea surface temperature increment value of the critical sea surface temperature zone, and SSTM(y,ti,pi) represents the ith year with latitude and longitude falling within the critical sea surface temperature zone. Monthly sea surface temperature; The sea surface temperature index (SSTBS) of key sea surface temperature zones is standardized to obtain the SSTBS value. 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 increase of sea surface temperature in the key sea surface temperature zone, μ(SSTBS) is the average value of SSTBS from year (YN-1) to year Y, and σ(SSTBS) is the standard deviation of SSTBS from year (YN-1) to year Y.

[0031] The method for obtaining the soil moisture index includes: Key months for soil moisture extraction from the monthly scale of the annual increment of soil moisture (DSW). The soil moisture data (SWM) is calculated using the following formula: ; 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 SWM(y,t,p) is the year at the grid point of the t-th latitude and p-th longitude. The interannual increase in soil moisture in a given month, DSW(y, (t, p) represents the year y at the grid point with latitude t and longitude p. The annual increase in soil moisture in each month =1,2,3,...,12; Based on the interannual increment of soil moisture (SWM), the SWM index of latitudinal and longitude grid points simultaneously located within the critical soil moisture zone is extracted and averaged to obtain the interannual increment of soil moisture (SWBS) in the critical soil moisture zone. The calculation formula is as follows: ; In the formula: y represents the year, n3 represents the number of grid points whose latitude and longitude fall within the critical soil moisture zone, (ti,pi) represents the i-th grid point whose latitude and longitude fall within the critical soil moisture zone, i=1,2,3,...,n1, SWBS represents the interannual increment of soil moisture in the critical soil moisture zone, and SWM(y,ti,pi) represents the ith year whose y-th point falls within the critical soil moisture zone. Monthly soil moisture value; The interannual increment of soil moisture in key soil moisture zones was standardized using the Soil Moisture Baseline (SWBS) to obtain the Soil Moisture Index, which is calculated using the following formula: ; In the formula: y is the year, ZSWBS is the soil moisture index, Z is the standardization process, SWBS is the interannual increment of soil moisture in the key soil moisture zone, μ(SWBS) is the average value of SWBS from year YN-1 to year Y, and σSWBS is the standard deviation of SWBS from year (YN-1) to year Y.

[0032] In this embodiment, monthly data on temperature, precipitation, sea ice concentration, sea surface temperature, and soil moisture from 1963 to 2022 are used, along with global sea ice concentration data for March 2023, global sea surface temperature data for February 2023, and soil moisture data for Siberia for April 2023, to predict the complex high-temperature and drought conditions in Northeast China during the summer of 2023. A detailed explanation is provided, focusing on specific regions. Step 1: Obtain the standardized composite high temperature and drought index for the summer of 2022 in Northeast China. ; Gridded monthly temperature and precipitation data for Northeast China from 1963 to 2022 were collected using the CN05.1 dataset, with a horizontal resolution of 0.25°. 0.25°; Based on gridded monthly temperature and precipitation data from 1963 to 2022, cumulative probability density functions for temperature and precipitation are constructed. The cumulative probability density function formulas for temperature and precipitation are as follows: ; In the formula: Let be the cumulative probability density function of temperature and precipitation, i=1,2, where when i=1, Let be the cumulative probability density function of temperature. When i=2, Let be the cumulative probability density function of precipitation; P represents the calculation process of the cumulative probability density function. Let i be the cumulative distribution of high temperature events or insufficient precipitation events, i=1,2, where when i=1, ... For the cumulative distribution of high-temperature events, when i=2, The cumulative distribution of insufficient precipitation events; Through the cumulative probability density function of temperature and precipitation Calculate the individual regression periods for high-temperature events and insufficient precipitation events separately. The formulas for calculating the individual regression periods of high-temperature events and insufficient precipitation events are as follows: ; In the formula: For high temperature events or insufficient precipitation events, the individual regression period is i=1,2, where when i=1, For the individual regression period of a high-temperature event, when i=2, The individual regression period for insufficient precipitation events; The cumulative probability density functions for calculated temperature and precipitation; Based on the obtained cumulative probability density distribution functions of temperature and precipitation The survival cumulative probability density distribution functions of temperature and precipitation were calculated. The calculation formula is as follows: ; In the formula: Let be the cumulative probability density distribution function for survival of temperature and precipitation, i=1,2, where when i=1, Let be the survival cumulative probability density distribution function of temperature. When i=2, Let be the cumulative probability density distribution function for precipitation. Let be the cumulative probability density function of temperature and precipitation, i=1,2, where when i=1, Let be the cumulative probability density function of temperature. When i=2, Let be the cumulative probability density function of precipitation; Using the t-copula method, the joint survival cumulative probability density function of the complex high-temperature drought event was calculated based on the calculated survival cumulative probability density distribution functions of air temperature and precipitation. This joint survival cumulative probability density function was defined as the complex high-temperature drought index PI, representing the intensity of the complex high-temperature drought in Northeast China from 1963 to 2022. The calculation formula is as follows: ; In the formula: PI is the composite high-temperature drought index, representing the intensity of combined high-temperature drought in Northeast China from 1963 to 2022; C is the process of calculating the composite high-temperature drought index using the t-copula method. Let be the cumulative probability density function for survival based on temperature. Let be the cumulative probability density function for precipitation; P represents the calculation process for the cumulative probability density function. This represents the combined cumulative distribution of high-temperature events and insufficient precipitation events.

[0033] The composite high temperature and drought index (PI) for each of the 12 months from 1963 to 2022 was used to extract the index for June, July, and August of each year, forming the composite high temperature and drought index. The data obtained by averaging the monthly data is denoted as the composite high temperature and drought index data for Northeast China in summer, PIJJA. The calculation formula is as follows: ; In the formula: y is the year, t is the t-th latitude of Northeast China, p is the p-th longitude of Northeast China, PIJJA y，t，p Let PI be the composite high temperature and drought index for the summer of year y at the grid point at latitude t and longitude p. y，6，t，p Let PI be the composite high temperature and drought index for June of year y at the grid point at latitude t and longitude p. y，7，t，p Let PI be the composite high temperature and drought index for July of year y at the grid point at latitude t and longitude p. y，8，t，p Let be the composite high temperature and drought index for August of year y at the grid point at latitude t and longitude p.

[0034] Further standardization of the composite high temperature and drought index PIJJA for the summers of Northeast China from 1963 to 2022 yields the standardized composite high temperature and drought index ZPIJJA_NEC for the summers of Northeast China from 1963 to 2022. The calculation formula is as follows: ; In the formula: y is the year, ZPIJJA_NEC is the standardized composite high temperature and drought index for summer in Northeast China from 1963 to 2022, Z is the standardization process, PIJJA is the composite high temperature and drought index for mid-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 forecast factor indices from 1963 to 2023, including the sea ice concentration index, sea surface temperature index, and soil moisture index; Monthly sea ice concentration (SIC) and sea surface temperature (SST) data for the world from 1963 to 2023 were collected using the Hadley dataset, with a horizontal resolution of 1°. 1°; Monthly soil moisture (SW) data from 1963 to 2023 worldwide were collected using the ERA5 dataset, with a horizontal resolution of 1°. 1°; The interannual increment values ​​of sea ice concentration (SIC), sea surface temperature (SST), and soil moisture (SW) from 1963 to 2023 were processed using the interannual increment method to obtain the interannual increment values ​​of sea ice concentration (DSIC), sea surface temperature (DSST), and soil moisture (DSW). 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 previous meteorological factor data in the form of interannual increment, XXX=SIC, SST, SW; The sea ice concentration data SICM for March, the key month for sea ice, is extracted from the monthly scale of the interannual sea ice concentration increment (DSIC). The calculation formula is as follows: ; In the formula: y represents the year, t represents the t-th latitude (t=1,2,3,...,181), p represents the p-th longitude (p=1,2,3,...,360), SICM y,t,p Let DSIC represent the interannual increase in sea ice concentration in March of year y at the grid point of latitude t and longitude p. y,3,t,p Let be the interannual increment of sea ice concentration in March of year y at the grid point of latitude t and longitude p; The key area for sea ice concentration in the Barents Sea is defined as: 30°E–60°E, 72°N–78°N. Figure 2 (The black rectangle in the middle) Based on the interannual sea ice concentration increment (SICM) value, the SICM values ​​of latitude and longitude grid points simultaneously located within the Barents Sea sea ice concentration critical area are extracted and averaged to obtain the interannual sea ice concentration increment (SICBS) value of the Barents Sea sea ice concentration critical area. The calculation formula is as follows: ; In the formula: y represents the year, n1 represents the number of grid points whose latitude and longitude fall within the Barents Sea sea ice concentration critical area, (ti,pi) represents the i-th grid point whose latitude and longitude fall within the sea ice concentration critical area, i=1,2,3,...,n1, SICBS represents the interannual increment of sea ice concentration in the Barents Sea sea ice concentration critical area, and SICM(y,ti,pi) represents the sea ice concentration value in March of the i-th year y that falls within the Barents Sea sea ice concentration critical area, i=1,2,3,...,n1; The interannual increment of sea ice concentration (SICBS) in the key area of ​​Barents Sea ice concentration is standardized to obtain the sea ice concentration index, which is calculated using the following formula: ; In the formula: y is the year, ZSICBS is the sea ice concentration index, Z is the standardization process, SICBS is the interannual increment of sea ice concentration in the key area of ​​sea ice concentration in the Barents Sea, μ(SICBS) is the average value of SICBS from year YN-1 to year Y, and σ(SICBS) is the standard deviation of SICBS from year YN-1 to year Y.

[0036] The Sea Temperature Data for February (SSTM), the key month for Sea Temperature, is extracted from the monthly scale of the annual Sea Temperature Segregation (DSST) value. The calculation formula is as follows: ; In the formula: y represents the year, t represents the t-th latitude (t=1,2,3,...,181), p represents the p-th longitude (p=1,2,3,...,360), and SSTM y,t,p Let DSST be the interannual increase of sea surface temperature in February of year y at the grid point of latitude t and longitude p. y,2,t,p Let be the interannual increase of sea surface temperature in February of year y at the grid point of latitude t and longitude p; The tropical Indian Ocean and tropical Pacific Ocean regions are defined as: 30°E–60°W, 30°S–30°N ( Figure 3 (The black rectangle in the middle) Based on the interannual sea surface temperature increment (SSTM) value, the SSTM values ​​of latitude and longitude grid points simultaneously located in the tropical Indian Ocean and tropical Pacific Ocean are extracted and averaged to obtain the interannual sea surface temperature increment (SSTBS) value for the tropical Indian Ocean and tropical Pacific Ocean. The calculation formula is as follows: ; In the formula: y is the year, n2 is the number of grid points with latitude and longitude falling in the tropical Indian Ocean and tropical Pacific Ocean, (ti,pi) is the i-th grid point with latitude and longitude falling in the tropical Indian Ocean and tropical Pacific Ocean, i=1,2,3,...,n2, SSTBS is the interannual increase of sea surface temperature in the tropical Indian Ocean and tropical Pacific Ocean, and SSTM(y,ti,pi) is the sea surface temperature value in February of the i-th year y that falls in the tropical Indian Ocean and tropical Pacific Ocean, i=1,2,3,...,n2; The sea surface temperature index (SSTBS) of key sea surface temperature zones is standardized to obtain the SSTBS value. 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 increase in sea surface temperature in the tropical Indian Ocean and 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] Soil moisture data for April, the key month for soil moisture, is extracted from the monthly scale of the interannual increment of soil moisture (DSW). The calculation formula is as follows: ; In the formula: y represents the year, t represents the t-th latitude (t=1,2,3,...,181), p represents the p-th longitude (p=1,2,3,...,360), SWM y,t,p Let DSW be the interannual increment of soil moisture in April of year y at the grid point of latitude t and longitude p. y,4,t,p Let be the interannual increment of soil moisture in April of year y at the grid point of latitude t and longitude p; The key zone for soil moisture in northwestern Siberia is defined as: 60°E–120°E, 60°N–75°N. Figure 4 (The black rectangle in the middle) Based on the interannual increment of soil moisture (SWM), the SWM data of latitudinal and longitude grid points simultaneously located within the key soil moisture zone of northwestern Siberia are extracted and averaged to obtain the interannual increment of soil moisture (SWBS) for the key soil moisture zone of northwestern Siberia. The calculation formula is as follows: ; In the formula: y is the year, n3 is the number of grid points whose latitude and longitude fall within the key soil moisture zone of northwestern Siberia, (ti,pi) is the i-th grid point whose latitude and longitude fall within the key soil moisture zone of northwestern Siberia, i=1,2,3,...,n1, SWBS is the interannual increment of soil moisture in the key soil moisture zone of northwestern Siberia, and SWM(y,ti,pi) is the soil moisture value in April of the i-th year that falls within the key soil moisture zone of northwestern Siberia, i=1,2,3,...,n1; The interannual increment of soil moisture in key soil moisture zones was standardized using the Soil Moisture Baseline (SWBS) to obtain the Soil Moisture Index, which is calculated using the following formula: ; In the formula: y is the year, ZSWBS is the soil moisture index, Z is the standardization process, SWBS is the interannual increment of soil moisture in the key soil moisture area of ​​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 forecast factor index for 2023 into the forecast model for the forecast quantity DPI pre-built based on the forecast factor index of previous years, and predict the DPI forecast value for 2023. Based on the standardized composite high temperature and drought index ZPIJJA_NEC for the summers of Northeast China from 1963 to 2022 obtained in Step 1 above, the standardized composite high temperature and drought interannual increment index DPI for the target region from 1964 to 2022 in interannual increment form is defined using the interannual increment method. The calculation formula is as follows: ; In the formula: y is the year, DPI is the standardized compound high temperature drought interannual increment index for the summer of 1964 to 2022 in Northeast China, and ZPIJJA_NEC is the standardized compound high temperature drought index for the summer of 1963 to 2022 in Northeast China. Using the sea ice concentration index ZSICBS, sea surface temperature index ZSSTBS, and soil moisture index ZSWBS calculated in step two above from 1964 to 2022 as forecasting factor indices and DPI as the forecast quantity, after confirming the independence among the forecasting factors, a forecasting model for the forecast quantity DPI is constructed based on the multiple linear regression method, where the forecasting equation is: ; In the formula: y represents the year, and DPI is the predicted value of the standardized composite high-temperature drought interannual increment index for the summers of Northeast China from 1964 to 2022. , , The forecast factor index is defined.

[0039] Based on the forecast factor indices ZSICBS, ZSSTBS, and ZSWBS calculated in step two above, the 2023 forecast factor indices are input into the forecast model for the forecast quantity DPI constructed based on the forecast factor indices of previous years to obtain the 2023 DPI forecast value. The calculation formula is as follows: ; In the formula: This is the predicted value of the standardized compound high temperature and drought interannual increment index for the summer of 2023 in Northeast China. , , These are the various forecast factor indices for 2023.

[0040] Step 4: Combine high temperature and drought data Adding this to the predicted DPI value for 2023 yields the predicted composite high temperature and drought index for 2023. ; The standardized composite high-temperature drought interannual increment index forecast values ​​for Northeast China in the summer of 2023, obtained in step three above, are used as the basis for this forecast. Add the standardized composite high temperature and drought index for Northeast China in the summer of 2022 obtained in step one above. The predicted value for year Y is obtained. The calculation formula is as follows: ; In the formula: ZPIJJA_REGION 2023 The standardized composite high temperature and drought index (DPI) forecast for Northeast China in the summer of 2023 is given. 2023 ZPIJJA_REGION is the predicted value of the standardized compound high-temperature drought interannual increment index for Northeast China in the summer of 2023. 2022 This represents the actual value of the standardized composite high temperature and drought index for Northeast China in the summer of 2022.

[0041] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A dynamic-based method for predicting composite high-temperature drought in target areas, characterized in that, The method includes: Obtain the standardized composite high temperature and drought index ZPIJJA_REGION for the target region in summer of year Y-1. Y-1 ; Obtain forecast factor indices from previous years and the forecast year Y, including sea ice concentration index, sea surface temperature index, and soil moisture index; The forecast factor index of the year Y to be predicted is input into the forecast model of the forecast quantity DPI pre-built based on the forecast factor index of previous years to predict the DPI forecast value of year Y. The composite high temperature and drought index ZPIJJA_REGION Y-1 Adding this to the predicted DPI value for year Y yields the predicted composite high temperature and drought index value ZPIJJA_REGION for year Y. Y ; Where Y represents the combined high temperature and drought year in the target region to be predicted; Methods for obtaining forecast factor indices for previous years and the year Y to be forecasted include: Monthly sea ice concentration (SIC) and sea surface temperature (SST) data were collected globally from year YN to year Y using the Hadley dataset, with a horizontal resolution of 1°. 1°; Monthly soil moisture (SW) data from YN to Y were collected globally using the ERA5 dataset, with a horizontal resolution of 1°. 1°; N represents the time span of data collection, in years; The interannual increments of sea ice concentration (SIC), sea surface temperature (SST), and soil moisture (SW) from YN to Y were processed using the interannual increment method to obtain the interannual increment values ​​of sea ice concentration (DSIC), sea surface temperature (DSST), and soil moisture (DSW) in the form of interannual 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 previous meteorological factor data in the form of interannual increment, DXXX=DSIC, DSST, DSW, XXX is the previous meteorological factor data, 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 increments, the sea ice concentration index, sea surface temperature index, and soil moisture index are calculated. The method for obtaining the sea ice concentration index includes: Extracting key months for sea ice from the monthly scale of the annual sea ice concentration increment DSIC. The calculation formula for the SICM sea ice concentration data 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 year at the grid point of the t-th latitude and p-th longitude. The interannual increase in sea ice concentration for each month, DSIC(y, (t, p) represents the year y at the grid point with latitude t and longitude p. The annual increase in sea ice concentration in the month, =1,2,3,...,12; Based on the interannual sea ice concentration increment (SICM) value, the SICM values ​​of latitude and longitude grid points simultaneously located within the critical sea ice concentration region are extracted and averaged to obtain the interannual sea ice concentration increment (SICBS) value for the critical sea ice concentration region. The calculation formula is as follows: ; In the formula: y represents the year, n1 represents the number of grid points whose latitude and longitude fall within the critical sea ice concentration zone, (ti,pi) represents the i-th grid point whose latitude and longitude fall within the critical sea ice concentration zone, i=1,2,3,...,n1, SICBS represents the interannual increment of sea ice concentration in the critical sea ice concentration zone, and SICM(y,ti,pi) represents the i-th year whose latitude and longitude fall within the critical sea ice concentration zone. Monthly sea ice concentration values; The SICBS (Sea Ice Concentration Index) values ​​of the interannual increase in sea ice concentration in key sea ice concentration areas are standardized to obtain the sea ice concentration index, which is calculated using the following formula: ； In the formula: y is the year, ZSICBS is the sea ice concentration index, Z is the standardization process, SICBS is the interannual increment of sea ice concentration in the key sea ice concentration area, μ(SICBS) is the average value of SICBS from year (YN-1) to year Y, and σ(SICBS) is the standard deviation of SICBS from year (YN-1) to year Y.

2. The target area composite high temperature drought prediction method based on dynamics according to claim 1, characterized in that, Obtain the standardized composite high temperature and drought index ZPIJJA_REGION for the target region in summer of year Y-1. Y-1 ,include: Monthly gridded temperature and precipitation data for the target region from YN to Y-1, preceding the forecast year Y, were collected using the CN05.1 dataset. The horizontal resolution of the data is 0.25°. 0.25°; N represents the time span of data collection, in years; Based on the gridded monthly temperature and precipitation data, and using the t-coupla method, a composite high-temperature drought index PI is constructed to characterize the intensity of composite high-temperature drought in the target area from YN to Y-1, based on the joint cumulative probability density function. The composite high temperature and drought index PI for each of the 12 months from year YN to year Y-1 was extracted to obtain the index for June, July and August of each year. The index was averaged over the monthly dimension to obtain the data PIJJA representing the intensity of summer composite high temperature and drought in the target area. After standardizing the composite high temperature and drought data PIJJA of the target area from summer of year YN to summer of year Y-1, the standardized composite high temperature and drought index ZPIJJA_REGION of the target area from summer of year YN to summer of year Y-1 is obtained.

3. The target area composite high temperature drought prediction method based on dynamics according to claim 2, characterized in that, Construct a composite high-temperature drought index (PI) representing the intensity of combined high-temperature drought in the target region from year YN to year Y-1, including: Construct the cumulative probability density function of temperature and precipitation The cumulative probability density function formulas for temperature and precipitation are as follows: ； In the formula: Let be the cumulative probability density function of temperature and precipitation, i=1,2, where when i=1, Let be the cumulative probability density function of temperature. When i=2, Let be the cumulative probability density function of precipitation; P represents the calculation process of the cumulative probability density function. Let i be the cumulative distribution of high temperature events or insufficient precipitation events, i=1,2, where when i=1, ... For the cumulative distribution of high-temperature events, when i=2, The cumulative distribution of insufficient precipitation events; Through the cumulative probability density function of temperature and precipitation Calculate the individual regression periods for high-temperature events and insufficient precipitation events separately. The formulas for calculating the individual regression periods of high-temperature events and insufficient precipitation events are as follows: ； In the formula: For high temperature events or insufficient precipitation events, the individual regression period is i=1,2, where when i=1, For the individual regression period of a high-temperature event, when i=2, The individual regression period for insufficient precipitation events; The cumulative probability density functions for calculated temperature and precipitation; Based on the obtained cumulative probability density distribution functions of temperature and precipitation The survival cumulative probability density distribution functions of temperature and precipitation were calculated. The calculation formula is as follows: ； In the formula: Let be the cumulative probability density distribution function for survival of temperature and precipitation, i=1,2, where when i=1, Let be the survival cumulative probability density distribution function of temperature. When i=2, Let be the cumulative probability density distribution function for precipitation. Let be the cumulative probability density function of temperature and precipitation, i=1,2, where when i=1, Let be the cumulative probability density function of temperature. When i=2, Let be the cumulative probability density function of precipitation; Using the t-copula method, the joint cumulative probability density function of the complex high-temperature drought event is calculated based on the calculated cumulative probability density distribution functions of air temperature and precipitation. This joint cumulative probability density function is then defined as the complex high-temperature drought index PI, characterizing the intensity of the complex high-temperature drought. The calculation formula is as follows: ； In the formula: PI is the defined composite high temperature and drought index; C is the process of calculating the composite high temperature and drought index using the t-copula method. Let be the cumulative probability density function for survival based on temperature. Let be the cumulative probability density function for precipitation; P represents the calculation process for the cumulative probability density function. This represents the combined cumulative distribution of high-temperature events and insufficient precipitation events.

4. The target area composite high temperature drought prediction method based on dynamics according to claim 1, characterized in that, The predicted DPI forecast for year Y includes: The interannual increment index of the standardized forecast factor for year Y is calculated, and the forecast year is year Y; After all forecast factor indices have been calculated, the forecast factor indices for year Y are input into the forecast model for the forecast quantity DPI built based on the forecast factor indices 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, and DPI is the regression coefficient. Y x1 represents the predicted value of the standardized interannual increment index of high temperature and drought in the target region during the summer of year Y. Y x2 Y x3 Y These are the various forecast factor indices for year Y.

5. The target area composite high temperature drought prediction method based on dynamics according to claim 1, characterized in that, The predicted value of the composite high temperature and drought index for year Y is obtained as ZPIJJA_REGION. Y ,include: The predicted standardized interannual increment index (DPI) of the composite high temperature and drought in the target region during the summer of year Y is used. Y Add the standardized composite high temperature and drought index ZPIJJA_REGION for the summer of year Y-1 in the target region Y-1 The predicted value ZPIJJA_REGION for year Y is obtained. Y The calculation formula is as follows: ； In the formula: Y is the predicted year, ZPIJJA_REGION Y DPI is the standardized composite high temperature and drought index forecast value for the target region in summer of year Y. Y ZPIJJA_REGION is the predicted value of the standardized interannual increment index of high temperature and drought in the summer of year Y for the target region. Y-1 The value of the standardized composite high temperature and drought index for the target region in the summer of year Y-1 is given.

6. The target area composite high temperature drought prediction method based on dynamics according to claim 4, characterized in that, The method for constructing the forecast model for the predicted quantity DPI includes: Based on the standardized compound high temperature and drought index ZPIJJA_REGION for the summer of YN to Y-1 in the target region, the standardized compound high temperature and drought interannual increment index DPI for the summer of YN-1 to Y-1 in the target region is defined using the interannual increment method. The calculation formula is as follows: ； In the formula: y is the year, DPI is the standardized compound high temperature and drought interannual increment index of the target region from year YN-1 to summer of year Y-1, and ZPIJJA_REGION is the standardized compound high temperature and drought index of the target region from year YN to summer of year Y-1. Using the sea ice concentration index, sea surface temperature index, and soil moisture index from previous years YN-1 to Y-1 as forecasting factors, and DPI as the forecast quantity, after confirming the independence among the forecasting factors, a forecasting model for the forecast quantity DPI is constructed based on the multiple linear regression method, where the forecasting 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 interannual increment index for the summer of the target region from year YN-1 to year Y-1, and x1, x2, and x3 are the defined forecast factor indices.

7. The target area composite high temperature drought prediction method based on dynamics according to claim 1, characterized in that, The method for obtaining the sea surface temperature index includes: Extracting key months of sea surface temperature (DSST) on a monthly scale from the annual sea surface temperature increment. The calculation formula for the SSTM sea surface temperature data 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 SSTM(y,t,p) represents the year y at the grid point of the t-th latitude and p-th longitude. The interannual increase in sea surface temperature in a given month, DSST(y, (t, p) represents the year y at the grid point with latitude t and longitude p. The annual increase in sea surface temperature in the month, =1,2,3,...,12; Based on the interannual sea surface temperature increment (SSTM) value, the SSTM values ​​of latitude and longitude grid points simultaneously located within the key sea surface temperature region are extracted and averaged to obtain the interannual sea surface temperature increment (SSTBS) value of the key sea surface temperature region. The calculation formula is as follows: ; In the formula: y represents the year, n2 represents the number of grid points with latitude and longitude falling within the critical sea surface temperature zone, (ti,pi) represents the i-th grid point with latitude and longitude falling within the critical sea surface temperature zone, i=1,2,3,...,n2, SSTBS represents the interannual sea surface temperature increment value of the critical sea surface temperature zone, and SSTM(y,ti,pi) represents the ith year with latitude and longitude falling within the critical sea surface temperature zone. Monthly sea surface temperature; The sea surface temperature index (SSTBS) of key sea surface temperature zones is standardized to obtain the SSTBS value. 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 increase of sea surface temperature in the key sea surface temperature zone, μ(SSTBS) is the average value of SSTBS from year (YN-1) to year Y, and σ(SSTBS) is the standard deviation of SSTBS from year (YN-1) to year Y.

8. The target area composite high temperature drought prediction method based on dynamics according to claim 1, characterized in that, The method for obtaining the soil moisture index includes: Key months for soil moisture extraction from the monthly scale of the annual increment of soil moisture (DSW). The soil moisture data (SWM) is calculated using the following formula: ； 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 SWM(y,t,p) is the year at the grid point of the t-th latitude and p-th longitude. The interannual increase in soil moisture in a given month, DSW(y, (t, p) represents the year y at the grid point with latitude t and longitude p. The annual increase in soil moisture in each month =1,2,3,...,12; Based on the interannual increment of soil moisture (SWM), the SWM index of latitudinal and longitude grid points simultaneously located within the critical soil moisture zone is extracted and averaged to obtain the interannual increment of soil moisture (SWBS) in the critical soil moisture zone. The calculation formula is as follows: ； In the formula: y represents the year, n3 represents the number of grid points whose latitude and longitude fall within the critical soil moisture zone, (ti,pi) represents the i-th grid point whose latitude and longitude fall within the critical soil moisture zone, i=1,2,3,...,n1, SWBS represents the interannual increment of soil moisture in the critical soil moisture zone, and SWM(y,ti,pi) represents the ith year whose y-th point falls within the critical soil moisture zone. Monthly soil moisture value; The interannual increment of soil moisture in key soil moisture zones was standardized using the Soil Moisture Baseline (SWBS) to obtain the Soil Moisture Index, which is calculated using the following formula: ； In the formula: y is the year, ZSWBS is the soil moisture index, Z is the standardization process, SWBS is the interannual increment of soil moisture in the key soil moisture zone, μ(SWBS) is the average value of SWBS from year YN-1 to year Y, and σSWBS is the standard deviation of SWBS from year (YN-1) to year Y.

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