A method for detecting climate change signals of drought events under high temperature and dry conditions

Abstract

The present invention relates to the field of carbon neutrality and discloses a method for detecting climate change signals of drought events under high-temperature-dry conditions. The method comprises the following steps: data collection; evaluating the dependency relationship and severity of high-temperature-atmospheric-dryness composite events; evaluating the response of regional extreme drought to changes in high-temperature-atmospheric-dryness conditions; estimating the probability of occurrence of different levels of high-temperature-atmospheric-dryness conditions in the future; quantifying the probability of historical and future regional extreme high-temperature-atmospheric-dryness events exceeding observed historical extremes; clarifying the dominant factors affecting changes in historical periods; identifying human activity signals of regional high-temperature-atmospheric-dryness composite events; and analyzing whether emission control can limit the increase in the severity of high-temperature-atmospheric-dryness composite events. The present invention has the beneficial effects of quantifying the additional impact of high temperature and atmospheric dryness on drought from an urgency perspective, clarifying the average state of high temperature and atmospheric dryness when drought occurs, and clarifying the dominant role of high temperature and atmospheric dryness on drought.

Description

Technical Field

[0001] The present invention relates to the field of carbon neutrality, and in particular to a method for detecting climate change signals of drought events under high temperature and dry conditions. Background Art

[0002] Against the backdrop of global warming, combined high-temperature, atmospheric dryness, and drought events are a hot topic of current research and are receiving widespread attention. The simultaneous occurrence of these conditions can increase the duration and severity of droughts, resulting in significant socioeconomic losses. However, research on the response of regional extreme droughts to these conditions is currently lacking. While studies have detected human activity signals in changes in precipitation and temperature, as key factors contributing to drought, research on these factors is lacking in this context. With continued greenhouse gas emissions, the probability and severity of combined high-temperature, atmospheric dryness events are expected to increase further. Exploring the role of controlling temperature rise and greenhouse gas emissions in mitigating the severity of these combined events emphasizes the importance of energy conservation and emission reduction. Studying how drought responds to these increasing high-temperature, atmospheric dryness events can provide a basis for decision-makers, thereby optimizing disaster prevention and mitigation measures and reducing the pressure and economic losses caused by extreme climate events on the ecological environment. Summary of the Invention

[0003] The purpose of the present invention is to propose a method for detecting climate change signals of drought events under high temperature and dry conditions, so as to solve the technical problem of the current lack of research on the response of regional extreme drought to high temperature and atmospheric dry conditions.

[0004] The present invention provides a method for detecting climate change signals of drought events under high temperature and dry conditions, comprising the following steps:

[0005] Step S1: Data collection: collecting drought event climate change data;

[0006] Step S2: Combine the data obtained in step S1 to calculate the normalized time series of air temperature, precipitation, surface soil moisture and scPDSI, and evaluate the dependency relationship of the high temperature-atmospheric dryness composite event and the severity of the composite event;

[0007] Step S3: Response of regional extreme drought to high temperature and atmospheric dryness during the historical period; Combine the temperature, precipitation, surface soil moisture, and scPDSI normalized time series calculated in step S2 to assess the response of regional extreme drought to changes in high temperature and atmospheric dryness;

[0008] Step S4: Temporal patterns and dependencies of high temperature and atmospheric dryness complex conditions under the background of anthropogenic climate change; combining the normalized time series of temperature and precipitation obtained in step S2, and using the CMIP6 future scenarios, the probability of occurrence of different levels of high temperature and atmospheric dryness conditions in the future is estimated;

[0009] Step S5: Estimating the recurrence probability of regional extreme drought events driven by high temperature and atmospheric dryness; calculating the additional impact of high temperature and atmospheric dryness on drought, clarifying the driving factors affecting regional extreme drought; based on the driving factors, quantifying the probability of historical and future regional extreme high temperature-atmospheric dryness-drought events exceeding the observed historical period every 10 years;

[0010] Step S6: The spatiotemporal evolution characteristics and driving factors of the severity of the high temperature-atmospheric dryness complex event are analyzed. Combined with the precipitation and temperature data obtained in step S1, the Standardized Precipitation Index (SCI) and Standardized Temperature Index (STI) based on the original data and after removing the linear trend are calculated. The SCEI is then calculated based on these two indices to clarify the dominant factors affecting the historical changes in the SCEI.

[0011] Step S7: Detection of climate change signals of high temperature-atmospheric dryness composite events; using a detection and attribution method based on rotational empirical orthogonal decomposition and an optimal fingerprint method to identify human activity signals of regional high temperature-atmospheric dryness composite events.

[0012] A storage medium stores instructions and data for implementing a method for detecting climate change signals of drought events under high temperature and dry conditions.

[0013] A device for detecting climate change signals of drought events under high temperature and dry conditions, comprising: a processor and a storage medium; the processor loads and executes instructions and data in the storage medium to implement a method for detecting climate change signals of drought events under high temperature and dry conditions.

[0014] The beneficial effects provided by the present invention are:

[0015] (1) The present invention makes up for the lack of response of the probability of occurrence of regional extreme drought events to high temperature-atmospheric dryness in previous studies. And the present invention analyzes the changes in the probability of occurrence and dependence of high temperature-atmospheric dryness from a centennial perspective. The present invention uses a future high emission scenario to quantify the additional impact of high temperature and atmospheric dryness on drought from an urgency perspective, clarifies the average state of high temperature and atmospheric dryness when drought occurs, and clarifies the dominant role of high temperature and atmospheric dryness. The present invention uses two sets of observation-based data sets and single-model large sample data sets to increase the credibility of the results. The present invention is applicable to global and regional studies.

[0016] (2) This paper quantifies the severity of the combined high-temperature and atmospheric dryness event and uses a detection and attribution method based on rotated empirical orthogonal decomposition and an optimal fingerprint method to identify anthropogenic climate change signals. Furthermore, the severity of the high-temperature and atmospheric dryness event is analyzed under different temperature rise levels and emission scenarios, emphasizing the important role of controlling temperature rise and emissions in controlling the severity of the combined event. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic flow chart of the method of the present invention;

[0018] Figure 2 Schematic diagram of the standardized time series of temperature, precipitation, self-calibrated Palmer Drought Index, and surface soil moisture in the middle and lower reaches of the Yangtze River;

[0019] Figure 3 Schematic diagram of the relationship between temperature and precipitation in summer and autumn in the middle and lower reaches of the Yangtze River, and its changes in the past 20 years and the past century;

[0020] Figure 4 Schematic diagram of the three-dimensional relationship between temperature, precipitation, and drought in the middle and lower reaches of the Yangtze River during summer and autumn, and a comparison between the past 20 years and the past 100 years;

[0021] Figure 5 Schematic diagram of the impact of anthropogenic climate change on precipitation, temperature, and the probability of high-temperature and atmospheric dryness in the middle and lower reaches of the Yangtze River over the historical period based on model data;

[0022] Figure 6 Schematic diagram of future projections of different levels of high temperature and atmospheric dryness combined conditions under high emission scenarios;

[0023] Figure 7 Schematic diagram of the changes in high temperatures, atmospheric dryness, and droughts of the same magnitude as those in the summer and autumn of 2019 in the middle and lower reaches of the Yangtze River over the past two centuries;

[0024] Figure 8 Schematic diagram of the temporal evolution of the standardized temperature index, standardized precipitation index, and standardized composite event index in summer and autumn over the middle and lower reaches of the Yangtze River during the historical period;

[0025] Figure 9 Schematic diagram of the three-dimensional relationship between the standardized temperature index, standardized precipitation index, and standard composite event index in summer and autumn in the middle and lower reaches of the Yangtze River and their changes in different periods;

[0026] Figure 10 Schematic diagram of the changing trend of high temperature and atmospheric dryness complex events under different forcing scenarios based on model data;

[0027] Figure 11 Schematic diagram of the identification of anthropogenic climate change signals for high temperature and atmospheric dryness composite events based on the rotational empirical orthogonal decomposition detection attribution method;

[0028] Figure 12 Schematic diagram of quantitative detection and attribution of composite dry heat event index based on the optimal fingerprint method;

[0029] Figure 13 Schematic diagram of the predicted change trend of the composite dry heat event index under different temperature rise levels;

[0030] Figure 14 Schematic diagram of future projections of the standardized precipitation index, standardized temperature index, and composite dry-heat event index under different emission scenarios;

[0031] Figure 15 It is a working diagram of the hardware device of the present invention. DETAILED DESCRIPTION

[0032] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0033] Before formally explaining the present invention, the scheme of the present invention is first generally explained for easy understanding.

[0034] Please refer to Figure 1 , Figure 1 It is a schematic flow diagram of the method of the present invention.

[0035] The present invention provides a method for detecting climate change signals of drought events under high temperature and dry conditions, comprising the following steps:

[0036] Step S1: Data collection: collecting drought event climate change data;

[0037] As an embodiment, the present invention collects observation-based regional precipitation, temperature data and self-calibrated Palmer Drought Index (scPDSI) and surface soil moisture data; collects temperature, precipitation and surface soil moisture, latent heat flux, sensible heat flux, relative humidity, wind speed, maximum temperature, minimum temperature data of the sixth phase of the International Coupled Comparison Program CMIP6; as well as elevation data and effective soil moisture data; single model large sample set data comes from the Geophysical Fluid Dynamics Laboratory's Seamless System for Prediction and Earth System Research (SPEAR) data set, including precipitation and temperature data.

[0038] In step S1, to verify the reliability of the results, two sets of observational data of different durations were collected. The observational precipitation, temperature, and scPDSI data collected are on a centennial scale, aiming to increase the number of cases studied for regional extreme droughts and to compare changes over the past 20 years with historical periods. The collected single-model large-sample dataset uses a high-resolution model to improve the accuracy of regional-scale simulations and effectively validate the CMIP6 results. The single-model large-sample dataset only provides a combination of internal variability, avoiding model construction discrepancies within CMIP6 and reducing model uncertainty.

[0039] Step S2: Combine the data obtained in step S1 to calculate the normalized time series of air temperature, precipitation, surface soil moisture and scPDSI, and evaluate the dependency relationship of the high temperature-atmospheric dryness composite event and the severity of the composite event;

[0040] As an example, the present invention combines the data obtained in step S1 with the Penman-Montetus formula recommended by the Food and Agriculture Organization of the United Nations to calculate potential evapotranspiration. The scPDSI is calculated using potential evapotranspiration, precipitation, and available soil moisture. To facilitate horizontal comparisons between different variables and datasets, precipitation, air temperature, surface soil moisture, and the scPDSI are standardized. Using the standardized data, the probability of a combined low precipitation and high temperature occurrence is calculated. A copula function is used to calculate a probability multiplication factor for assessing the dependency of a high temperature-atmospheric dryness composite event, and a standardized composite event index (SCEI) for assessing the severity of the composite event.

[0041] In step S2, precipitation, air temperature, surface soil moisture, and scPDSI are standardized to obtain the standard deviation of the anomaly (y), which is calculated as follows:

[0042]

[0043] Where x is the time series of precipitation, temperature, surface soil moisture and scPDSI, x ref is the reference period time series, Expressed as the mean value of the reference period, detrend is linear detrending, and δ is the standard deviation. The calculated result is called the "standard deviation of the deviation" and its unit is set to "SD".

[0044] Using the CMIP6 variables: sensible heat flux, latent heat flux, relative humidity, wind speed, maximum temperature, and minimum temperature, combined with elevation data, the Penman-Montes formula is used to calculate PET, which is then combined with precipitation and effective soil moisture to calculate scPDSI. The Penman-Montes formula is as follows:

[0045]

[0046] Where Δ is the slope of the saturated water vapor pressure curve, R n is the net radiation of the surface, G is the soil heat flux density, T is the daily average temperature at 2 meters, γ is the psychrometric constant, u2 is the wind speed at 2 meters, and e s -e a is the difference between the saturated vapor pressure and the actual vapor pressure.

[0047] The Copula function is used to calculate the probability multiplication factor to measure the dependence of precipitation and temperature. The calculation formula is as follows:

[0048] p=f(T>prob 90 ∩P <prob 10 )

[0049] Where f represents the joint cumulative probability, T represents the temperature, P represents the precipitation, and prob 90 and prob 10 Represents the 90th or 10th percentile exceeding the reference period, respectively, and p represents the joint probability of temperature and precipitation under these conditions. The probability multiplication factor is the ratio of the joint probability to the probability assuming independent distributions. This step assumes that temperature and precipitation are greater than their 90th and less than their 10th percentiles, respectively. Therefore, the probability of independent distribution is 0.1 × 0.1. The probability multiplication factor of the present invention can ultimately be simplified to p / 0.01. A probability multiplication factor greater than 1 indicates a stronger dependence on high temperature and atmospheric dryness.

[0050] Similarly, the present invention uses a Copula function to construct the SCEI, using the standardized precipitation index and the standardized temperature index as variables, and the calculation formula is as follows:

[0051] P(X>x∩Y≤y)=P(Y≤y)-P(X≤x∩Y≤y)

[0052] Where P represents the joint probability distribution of the variables, X and Y represent the marginal probability distributions of the standardized temperature index and standardized precipitation index, respectively, and x and y represent the corresponding values ​​of the random variables. After calculating the joint probability, it is transformed using a standard normal distribution to obtain the SCEI, which is used to measure the severity of high temperature and atmospheric dryness.

[0053] Step S3: Response of regional extreme drought to high temperature and atmospheric dryness during the historical period; Combine the temperature, precipitation, surface soil moisture, and scPDSI normalized time series calculated in step S2 to assess the response of regional extreme drought to changes in high temperature and atmospheric dryness;

[0054] It should be noted that step S3 specifically includes the following steps: combining the temperature, precipitation, surface soil moisture, and scPDSI standardized time series calculated in step S2, comparing the changes in the probability of regional extreme drought occurring simultaneously with high temperature, atmospheric dryness, and high temperature-atmospheric dryness in the past 100 years and the past 20 years, and analyzing the temporal evolution characteristics of drought, high temperature, and atmospheric dryness in historical periods; constructing the coupling relationship between regional temperature and precipitation, and comparing the changes in this coupling relationship in the past 20 years with the past 100 years; combining scPDSI and surface soil moisture, constructing the three-dimensional relationship between temperature, precipitation, and drought and its changes, and reflecting the response of regional extreme drought to changes in high temperature-atmospheric dryness conditions through the distribution of regional extreme drought in the two-dimensional temperature-precipitation coordinates in different periods.

[0055] Step S4: Temporal patterns and dependencies of high temperature and atmospheric dryness complex conditions under the background of anthropogenic climate change; combining the normalized time series of temperature and precipitation obtained in step S2, and using the CMIP6 future scenarios, the probability of occurrence of different levels of high temperature and atmospheric dryness conditions in the future is estimated;

[0056] It should be noted that step S4 specifically includes: combining the standardized time series obtained in step S2, comparing the changing trends of different levels of high temperature-atmospheric dryness composite conditions in CMIP6 under historical full-forcing experiments and natural-forcing-only experiments, and analyzing the role of anthropogenic climate change in the high temperature-atmospheric dryness composite conditions; calculating the probability multiplication factor, and analyzing the changes in the interdependence between high temperature and atmospheric dryness conditions in the future relative to the historical period; and using future CMIP6 scenarios, including low, medium, and high emission scenarios, estimating the probability of occurrence of different levels of high temperature-atmospheric dryness conditions in the future.

[0057] In step S4, to facilitate comparison of the probability multiplication factors between historical and future periods, the historical period is used as the reference period for both periods. When predicting the probability of different levels of high temperature and atmospheric dryness in the future, a 200-year reference period is used, encompassing both the historical full forcing scenario and the future high emissions scenario, to mitigate biases caused by extreme temperature increases under future high emissions scenarios.

[0058] Step S5: Estimating the recurrence probability of regional extreme drought events driven by high temperature and atmospheric dryness; calculating the additional impact of high temperature and atmospheric dryness on drought, clarifying the driving factors affecting regional extreme drought; based on the driving factors, quantifying the probability of historical and future regional extreme high temperature-atmospheric dryness-drought events exceeding the observed historical period every 10 years;

[0059] It should be noted that in step S5, the recurrence probability of regional extreme drought composite events driven by high temperature and atmospheric dryness is estimated; the additional impact of high temperature and atmospheric dryness on drought is calculated respectively to clarify the most important driving factors affecting regional extreme drought; the most extreme high temperature-atmospheric dryness-drought event observed in regional history is used as the standard, and the regional average value of high temperature, atmospheric dryness and drought of this event is used as the evaluation threshold, and the model simulation data is used to calculate the historical and future probability of high temperature, atmospheric dryness and high temperature-atmospheric dryness composite conditions under the evaluation threshold; the change in the probability of this level of drought event occurring in the historical period and the future period of the model simulation data of multiple models is calculated; the change in the average state of future precipitation and temperature relative to the historical period when an extreme drought event occurs is evaluated to clarify the change in the degree of dependence of regional extreme drought on high temperature and atmospheric dryness under the high emission scenario; finally, the probability of the occurrence of extreme high temperature-atmospheric dryness-drought events exceeding the observed historical period is quantified every 10 years.

[0060] To calculate the additional effects of high temperature and atmospheric dryness, step S5 is as follows:

[0061] S51: Calculate the average values ​​of scPDSI and surface soil moisture under the conditions where the temperature exceeds the 90th percentile of the reference period and / or the precipitation is less than the 10th percentile of the reference period, respectively. The formula is as follows:

[0062] D dry-hot =mean(D|T>prob 90 ∩P<prob 10 )

[0063] D hot =mean(D|T>prob 90 )

[0064] D dry =mean(D|P<prob 10 )

[0065] Where D dry-hot represents the average value of scPDSI or surface soil moisture under the conditions where the temperature exceeds the 90th percentile of the reference period and the precipitation is less than the 10th percentile of the reference period, D hot and D dry are the average values ​​of scPDSI or surface soil moisture when only temperature exceeds the 90th percentile and only precipitation is less than the 10th percentile, respectively, and D represents drought measured by scPDSI or surface soil moisture.

[0066] S52: Additional effects of high temperatures and atmospheric dryness (AE hot and AE dry ) can be expressed as the following formula: AE dry=D dry-hot -D hot , AE hot =D dry-hot -D dry .

[0067] Step S6: The spatiotemporal evolution characteristics and driving factors of the severity of the high temperature-atmospheric dryness complex event are analyzed. Combined with the precipitation and temperature data obtained in step S1, the Standardized Precipitation Index (SCI) and Standardized Temperature Index (STI) based on the original data and after removing the linear trend are calculated. The SCEI is then calculated based on these two indices to clarify the dominant factors affecting the historical changes in the SCEI.

[0068] Combining the precipitation and temperature data obtained in step S1, the Standardized Precipitation Index (SPI) and Standardized Temperature Index (STI) are calculated based on the original data and the de-trended data, respectively. The SCEI is then calculated based on these two indices to quantify the temporal evolution of the severity of the combined high-temperature and atmospheric dryness events for both the original and de-trended data. The Pearson correlation coefficient between the SCEI calculated based on the original and de-trended data is calculated to determine whether fluctuations in the SCEI are caused by long-term trends or precipitation-temperature coupling. A three-dimensional relationship is constructed between the SCEI, STI, and SCEI. The historical period is divided into three distinct phases to determine the response of the SCEI to temperature and precipitation over different periods. Using multiple linear regression, the contribution of regional SPI and STI changes to SCEI changes is quantified, clarifying the dominant factors influencing SCEI changes over time.

[0069] To quantify the contribution of the standardized precipitation index and standardized temperature index to the SCEI, the specific formula of the multiple linear regression method in this step is as follows:

[0070] ΔSCEI=a×ΔSTI+b×ΔSPI+c

[0071] Where a and b are regression coefficients, c is a constant, Δ is the difference between the two periods, STI and SPI represent the standardized temperature index and standardized precipitation index, respectively.

[0072] Step S7: Detection of climate change signals of high temperature-atmospheric dryness composite events; using a detection and attribution method based on rotational empirical orthogonal decomposition and an optimal fingerprint method to identify human activity signals of regional high temperature-atmospheric dryness composite events.

[0073] Specifically, step S7 combines the regional spatial SCEI calculated in step 2 to perform a rotational empirical orthogonal decomposition on the three-dimensional spatiotemporal SCEI data of the grid points within the region to obtain a fingerprint pattern. The obtained fingerprint pattern is projected onto the observed SCEI, and the signal-to-noise ratio is quantified to calculate the significance level of the warming signal in the SCEI. The onset time of the warming signal is also obtained to verify the reliability of the signal.

[0074] Using a fingerprinting method, we compared the observed and simulated SCEI changes. Combined with the SCEI calculated from pre-industrial experiments, we used both univariate and bivariate optimal fingerprinting methods to assess the scaling factors of different external forcings and detect the signal of external forcing. Based on the scaling factors and SCEI trends, we quantified the contribution of external forcing to the observed SCEI changes.

[0075] The two detection attribution methods are as follows:

[0076] S71: A detection and attribution method based on rotating empirical orthogonal decomposition (ROE) is first used. Three-dimensional SCEI data from the CMIP6 historical full forcing and future high-emission scenarios are selected. Rotating empirical orthogonal decomposition is then used to extract the spatial fingerprint of the dominant mode, i.e., the dominant empirical orthogonal function of the spatial mode with the maximum variance obtained from the spatiotemporal covariance matrix. The spatial fingerprint is then projected onto the observed SCEI and the pre-industrial experimental SCEI using the following projection formula:

[0077]

[0078] where Proj(x) represents the projection value for each year, F(i, j) is the leading empirical orthogonal function, A(i, j) is the actual area of ​​each grid point, and SCEI(i, j, x) represents the observed or simulated SCEI value at each grid point at longitude i and latitude j.

[0079] The trend of the projected time series is then calculated, with the L-year trend values ​​of the observational data and the CMIP6 historical full forcing projection as the signal, S(L), and the standard deviation of the trend distribution of the L-year projection of the SCEI from the CMIP6 pre-industrial experiment as the noise, N(L). The dimensionless signal-to-noise ratio (SNR) is calculated as S(L) / N(L). L is a preset value; if the SNR exceeds 1.64 or 2.57, it indicates that the signal has been detected at the 90% and 99% confidence levels, respectively. Using model-based data and setting continuously increasing L values, the calculated SNR is used to calculate the time of signal onset, that is, the time when the forced signal begins to appear relative to natural climate variability.

[0080] S72: Detect and quantify the impact of external forcing on observed SCEI changes using the optimal fingerprint method. This method uses multiple linear regression and assumes that the observed changes are responses to external forcing and natural climate variability. The formula is as follows:

[0081]

[0082] Where y is the observed SCEI, X is the simulated SCEI under external forcing, is the sampling uncertainty of X, β is the scaling factor that adjusts the fingerprint to match the observation, and ε is the residual term associated with natural climate variability. The scaling factor is estimated using ordinary least squares and total least squares methods.

[0083] Step S8: Estimate the severity of future combined high-temperature and atmospheric dryness events under different temperature rise levels and emission scenarios. Combined with the CMI P6 temperature data obtained in step S1, calculate the temperature rise relative to the pre-industrial revolution (1861-1900), associate the SCEI with the temperature rise, and analyze the relationship between the severity of the combined high-temperature and atmospheric dryness and temperature rise. Combined with the SCEI calculated for different future CMI P6 emission scenarios obtained in S1 and S2, analyze whether emission controls can limit the increase in the severity of the combined high-temperature and atmospheric dryness event.

[0084] In step S8, the present method calculates the breakpoint in the severity trend of the combined high-temperature and atmospheric dryness event. This breakpoint is obtained using the parameter-free Pettitt test method. To explore the causes of the breakpoint in the trend of the combined high-temperature and atmospheric dryness event, the present method estimates the future trends of the Standardized Precipitation Index and the Standardized Temperature Index to clarify their impact on the SCEI.

[0085] As an example, the present invention further describes the present invention by taking the summer and autumn seasons (August–November) in the middle and lower reaches of the Yangtze River in China (110°–119°E, 25°–34°N) from 1901 to 2020 as an example. The implementation case is used to illustrate the present invention, but this case is not used to limit the scope of application of the present invention. It is also applicable to different regions and other time periods.

[0086] The specific steps of the method of the present invention are as follows:

[0087] (1) Collection of basic data;

[0088] In this example, the temperature and precipitation from the National Earth System Science Data Center, a national science and technology infrastructure platform, covering the period 1901–2020 with a resolution of 1 km were collected; the CN05 gridded observation dataset from the National Climate Change Research Center of China, covering the period 1961–2022 with a resolution of 0.25°; the scPDSI data version 4.07 from the Climate Research Unit (CRU), covering the period 1901–2022 with a resolution of 0.5°; and the surface soil moisture from the fifth generation European Reanalysis data (ERA5) of the European Centre for Medium-Range Weather Forecasts, covering the period 1961–2020 with a resolution of 0.1° were collected.

[0089] Based on data from CMIP6, historical and future experimental patterns of changes in the frequency of high temperature-atmospheric dryness events and droughts are shown in the following table:

[0090] Table 1 CMIP6 multi-model ensemble simulations used to assess dry and hot conditions and drought

[0091]

[0092]

[0093] The historical and future experimental patterns of changes in the severity of high temperature and atmospheric dryness events are shown in the following table:

[0094] Table 2 Multi-model ensemble used to assess the severity of high temperature-atmospheric dryness combined events

[0095]

[0096]

[0097] Note: * indicates that the data of this mode is available, - indicates that it is not available;

[0098] This example also collects precipitation and temperature data from the Geophysical Fluid Dynamics Laboratory's Seamless System for Prediction and Earth System Research (SPEAR) dataset, a total of 30 sets, including historical full forcing and future high emission scenario SSP585, covering 1921–2100 with a horizontal resolution of 0.5°.

[0099] (2) Response of regional extreme drought to high temperature and atmospheric dryness during historical periods

[0100] This example normalizes temperature, precipitation, scPDSI, and surface soil moisture. The resulting standard deviation is called the "anomaly standard deviation," with "SD" as its unit. Using 2001–2020 as the past 20 years and 1901–2000 as the past century, we find that the probability of extreme drought (scPDSI <–1) increased from 18% to 35% over the past century to the past 20 years. The probability of drought and atmospheric dryness occurring simultaneously during these two periods increased from 57% to 75%. However, when analyzing the probability of atmospheric dryness alone, there was no significant change between the two periods, at 21% and 20%, respectively (e.g., Figure 2 Although atmospheric dryness is a prerequisite for drought, high temperatures can promote the occurrence and development of drought. Based on this, it is assumed that high temperatures affect the probability of drought. The probability of high temperatures accompanying drought has increased from 33% to 100% in the past 20 years ( Figure 2 The probability of high temperature itself increased from 36% to 90%, which also led to an increase in the probability of high temperature-atmospheric dryness (from 15% to 45%; e.g. Figure 2 As shown). This proves that high temperature increases the probability of atmospheric dryness turning into drought. Further analysis of the temperature-precipitation coupling relationship and its changes shows that temperature and precipitation are negatively correlated, and the difference between the past 20 years and the past century reveals the increase in the probability of high temperature-atmospheric dryness coupling (such as Figure 3 On this basis, drought is added to construct a three-dimensional relationship between precipitation, temperature, and drought. In the past century, droughts were mainly distributed in areas with negative standard deviations of precipitation anomalies. In the past 20 years, the probability of droughts being distributed in areas with high temperatures and dry atmospheres has increased from 33.3% to 85.7%. CMIP6 provides more samples, and the probability of droughts being distributed in areas with high temperatures and dry atmospheres has increased from 44.3% to 73.4% in the two periods (see Figure 2). Figure 4 Changes in surface soil moisture confirm the scPDSI results. Changes in the relationship between drought and high-temperature, atmospheric dryness over historical periods reveal that high-temperature, atmospheric dryness is the primary driver of regional extreme drought.

[0101] (3) Temporal patterns and dependencies of high temperature and atmospheric dryness complex conditions driven by human factors

[0102] This case study uses model data to analyze the impact of anthropogenic climate change on the observed increase in the probability of a high-temperature and atmospheric dryness combination, and to estimate future high-temperature and atmospheric dryness combination conditions. Under the historical full forcing scenario, precipitation showed a significant downward trend of –0.49SD / century, and temperature increased significantly by 0.87SD / century (e.g., Figure 5However, when considering only natural forcing, neither precipitation nor temperature changes significantly. This reflects that human-driven precipitation decreases and temperature increases. The change in high temperature and atmospheric dryness increases by 17% per century, and also does not change significantly under natural forcing alone ( Figure 5 ), therefore, human-induced climate change has driven the increase of high temperature-atmospheric dryness composite conditions. In order to evaluate the changes in the probability and dependence of the simultaneous occurrence of high temperature and atmospheric dryness, the probability multiplication factor was further calculated. During the period 1961-2020, the average probability multiplication factor of the middle and lower reaches of the Yangtze River, the study area of ​​the implementation case, was 2.09, while in the future period 2041-2100, the probability multiplication factor was 11, an increase of 5.5 times, indicating that the probability and dependence of the simultaneous occurrence of high temperature-atmospheric dryness have greatly increased. Further predictions were made on the future development of high temperature-atmospheric dryness conditions, and four levels of high temperature-atmospheric dryness conditions were set, such as Figure 6 As shown, it can be found that the probability of atmospheric dry conditions and mild and moderate high temperature-atmospheric dry conditions in the future are decreasing, while the probability of extreme and abnormal high temperature-atmospheric dry conditions is increasing, and finally they all coincide with atmospheric dry conditions.

[0103] (4) Estimation of the recurrence probability of regional extreme drought complex events driven by high temperature and atmospheric dryness

[0104] The study area in this case study experienced its most severe observed summer-autumn drought in 2019, lasting from August to November and accompanied by the most severe atmospheric dryness and the second-most severe high temperature conditions in 120 years. This study set the thresholds for high temperature, atmospheric dryness, and drought to the regional averages of temperature, precipitation, and scPDSI (surface soil moisture) during the 2019 summer-autumn drought in the middle and lower reaches of the Yangtze River. Using these thresholds, model data were used to analyze the probability of a recurrence exceeding the severity of the 2019 summer-autumn drought and to clarify the role of high temperature and atmospheric dryness in drought. First, the impact of the combined effects of high temperature and atmospheric dryness on drought was calculated for both historical and future periods. It was found that for high temperature and atmospheric dryness conditions exceeding the same threshold, drought severity in the future period (2041–2100) was greater, with a regional average of –1.56SD, compared to –1SD in the historical period (1961–2020). Considering only the additional effect of high temperature, the resulting drought had a regional average of –0.1SD in the historical period and –0.001SD in the future. Considering only the additional impact of atmospheric dryness, the drought caused by the historical period was –0.54 SD, while the drought caused by the future period is even more severe, with a regional average of –0.91 SD. This indicates that atmospheric dryness will play a dominant role in the occurrence of drought in the future period. Due to the increased probability of high temperatures, drought is more sensitive to atmospheric dryness, meaning that the same degree of atmospheric dryness will cause more severe drought in the future. Further assessment of the change in the probability of the simultaneous occurrence of high temperature and atmospheric dryness exceeding the severity of 2019 shows that the probability of this level of condition increases from 0.05% in the historical period to 1.22% in the future period. This corresponds to an increase in the probability of high temperature from 0.73% to 91.6% and the probability of atmospheric dryness from 0.68% to 1.25%. This indicates that the increase in high temperature conditions in the future leads to an increase in the probability of combined high temperature and atmospheric dryness conditions.

[0105] The present invention further evaluates the probability of drought events exceeding the 2019 summer and autumn events in the historical and future periods using model data, such as Figure 7 As shown, the probability of droughts characterized by the scPDSI increased from 1.59% in the historical period to 17.82% in the future. Correspondingly, the high temperature regime during drought occurrence increased from 0.54SD to 7.48SD, and the atmospheric dryness regime increased from –0.76SD to –0.19SD. This suggests that due to the significantly increased probability of high temperatures in the future, even atmospheric dryness far below historical levels could trigger summer-autumn droughts of the same severity as in 2019. From a two-century perspective, the probability of both high temperature and atmospheric dryness increases from zero to 1.45% per century, while the probability of a drought exceeding the severity of 2019 increases from 2.3% in the 20th century to 14.9% in the 21st century.

[0106] (5) Spatiotemporal variation in the severity of high temperature-atmospheric dryness combined events and their driving factors

[0107] The present invention calculates the original time series and the time series without linear trend of the Standardized Precipitation Index (SPI), Standardized Temperature Index (STI) and Standardized Composite Event Index (SCEI), and calculates the Pearson correlation coefficient between the indices. Figure 8 As shown, from 1901 to 2022, the observed STI increased significantly, by 0.78 per century, while the SPI remained unchanged. The SCEI, under the combined influence, decreased by –0.81 per century, indicating an increase in the severity of combined high-temperature and atmospheric dryness events. From 1901 to 1960, the correlation coefficient between the SPI and STI was –0.08, while from 1961 to 2022, it was –0.19, indicating that the negative correlation between precipitation and temperature has intensified over the past 62 years. The correlation coefficient between the SCEI calculated from the de-trended SPI and STI and the SCEI calculated from the original data is 0.91, indicating that fluctuations in the SCEI are primarily influenced by the coupled relationship between high-temperature and atmospheric dryness. The three-dimensional spatial distribution of SPI, STI, and SCEI was constructed. The probability of the occurrence of extreme high temperature-atmospheric dryness composite events increased significantly after 1981, reaching 1.32%, compared with 0.59% in the previous two periods (1901–1950) and 0.57% in 1951–1980. It has increased significantly in the past 40 years (e.g. Figure 9 The distribution of extreme high temperature and atmospheric dryness composite events is toward higher temperatures, while natural forcing and detrended data alone do not show significant changes, indicating that anthropogenic climate change has increased the probability of high temperature and atmospheric dryness composite events and their dependence on higher temperatures.

[0108] This study uses multiple linear regression to quantitatively analyze the contribution of changes in the SPI and STI to changes in the SCEI. Between 1901 and 2020, observed STI dominated the SCEI variation, contributing 82.3%. Model-simulated STI remained the dominant factor, with historical full forcing contributing 60.3% and natural forcing alone contributing 57.1%. STI remains the dominant factor in the SCEI, regardless of whether anthropogenic climate change is considered. With further greenhouse gas emissions in the future, STI's contribution will increase further, reaching 79.4% under a low-emissions scenario and 88.4% under a high-emissions scenario.

[0109] (6) Spatiotemporal detection and attribution of anthropogenic climate change due to high temperature and atmospheric dryness combined events

[0110] The trend of the severity of the high temperature and atmospheric dryness complex events under the full historical forcing of CMIP6 is close to the observation value, with the SCEI decreasing by -0.7 per century. Under natural forcing only, the SCEI does not change significantly, which indicates that anthropogenic climate change has led to a decrease in the SCEI and aggravated the severity of the high temperature and atmospheric dryness complex events (e.g. Figure 10 shown).

[0111] Two detection and attribution methods are further used to identify the signal of anthropogenic climate change. Figure 11 (as shown in Figure 2), a spatial fingerprint of the dominant mode of three-dimensional spatiotemporal data at regional grid points under historical full forcing and future high-emission scenarios was extracted. This fingerprint pattern explains 97.8% of the total variance in the SCEI, effectively capturing the original SCEI signal. Due to the full forcing and high-emission scenarios employed, the fingerprint signal in this method is attributed to anthropogenic climate warming. Consistent with the overall regional decrease in the observed SCEI, the fingerprint exhibits generally negative values ​​over the middle and lower reaches of the Yangtze River, indicating an increase in the severity of the combined high-temperature and atmospheric dryness events. Projecting the spatial fingerprint pattern onto the observed SCEI reveals a significant upward trend, indicating that the anthropogenic climate warming signal can be captured. The calculated signal-to-noise ratio (SNR) is 3.77, indicating that the anthropogenic climate change signal can be detected with a 99% confidence interval. The estimated signal onset using the full forcing and high-emission scenarios indicates that the human activity signal is detected in 2017 and 2036, respectively, with 90% and 99% confidence intervals, validating the reliability of the anthropogenic climate warming signal in the historical period.

[0112] Another quantitative detection and attribution method is the optimal fingerprint method (e.g. Figure 12 (shown in Figure 2). The signals from both total and greenhouse gas forcing are reliably detected, with scaling factors for total forcing ranging from 1, indicating that total forcing effectively reproduces the observed SCEI change. In contrast, the signal from greenhouse gas forcing has a scaling factor less than 1, indicating that considering greenhouse gases alone overestimates the observed SCEI change. Bivariate detection separates the signal from natural forcing alone and reliably detects both total and greenhouse gas forcing. Total forcing contributes 83.6% of the decrease in SCEI between 1901 and 2020, while greenhouse gas forcing contributes 86.7%.

[0113] (7) Estimation of the severity of future high temperature and atmospheric dryness combined events

[0114] Considering that anthropogenic climate warming is the main driver of the severity of high-temperature and atmospheric dryness events, it is necessary to analyze how the SCEI responds to the continued increase in greenhouse gas emissions under future scenarios. Figure 13As shown, the SCEI continues to decline until reaching 3.5°C, after which it fluctuates around a very low value. If the temperature rise is controlled at 1.5°C or 2°C, the SCEI will be between –1.7 and –2.1, effectively controlling the temperature rise and helping to mitigate the worsening severity of the combined high temperature and atmospheric dryness event.

[0115] The present invention uses the non-parametric Pettitt test method to detect the mutation points of SCEI temporal changes in four future scenarios. Figure 14 As shown, under the four scenarios, the mutation point occurs around the 2050s. Before the mutation point, the SCEI shows a significant decline, falling from 1.92 to 4.3 per century from low to high emissions scenarios. After the mutation point, the SCEI fluctuates around low values, reaching –2.38 and –2.95 under low and high emissions scenarios, respectively. Emission control can effectively mitigate the severity of the combined high temperature and atmospheric dryness event.

[0116] To explore the factors behind changes in the SCEI, this study analyzed future time series of the SPI and STI. The STI showed a significant upward trend, increasing from 1.28 to 5.74 points per century, from low to high. The SPI also showed a significant upward trend, with the largest increase under the low-emissions scenario. Although the increase in the SPI partially mitigated the STI's contribution to the severity of combined high-temperature and atmospheric dryness events, under future emissions scenarios, when warming exceeds 3.5°C, the SCEI will fluctuate around a new normal severity level exceeding the most extreme values ​​in historical periods.

[0117] See Figure 15 , Figure 15 4 is a schematic diagram of the working of the hardware device of an embodiment of the present invention, wherein the hardware device specifically includes: a drought event climate change signal detection device 401 under high temperature and dry conditions, a processor 402 and a storage medium 403.

[0118] A device 401 for detecting climate change signals of drought events under high temperature and dry conditions: The device 401 for detecting climate change signals of drought events under high temperature and dry conditions implements the method for detecting climate change signals of drought events under high temperature and dry conditions.

[0119] Processor 402: The processor 402 loads and executes the instructions and data in the storage medium 403 to implement the method for detecting climate change signals of drought events under high temperature and dry conditions.

[0120] Storage medium 403: The storage medium 403 stores instructions and data; the storage medium 403 is used to implement the method for detecting climate change signals of drought events under high temperature and dry conditions.

[0121] The beneficial effects of the present invention are:

[0122] (1) The present invention makes up for the lack of response of the probability of occurrence of regional extreme drought events to high temperature-atmospheric dryness in previous studies. And the present invention analyzes the changes in the probability of occurrence and dependence of high temperature-atmospheric dryness from a centennial perspective. The present invention uses a future high emission scenario to quantify the additional impact of high temperature and atmospheric dryness on drought from an urgency perspective, clarifies the average state of high temperature and atmospheric dryness when drought occurs, and clarifies the dominant role of high temperature and atmospheric dryness. The present invention uses two sets of observation-based data sets and single-model large sample data sets to increase the credibility of the results. The present invention is applicable to global and regional studies.

[0123] (2) This paper quantifies the severity of the combined high-temperature and atmospheric dryness event and uses a detection and attribution method based on rotated empirical orthogonal decomposition and an optimal fingerprint method to identify anthropogenic climate change signals. Furthermore, the severity of the high-temperature and atmospheric dryness event is analyzed under different temperature rise levels and emission scenarios, emphasizing the important role of controlling temperature rise and emissions in controlling the severity of the combined event.

[0124] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for detecting climate change signals of drought events under high temperature and dry conditions, characterized by: The method comprises the following steps: Step S1: Data collection: collecting drought event climate change data; Step S2: Combine the data obtained in step S1 to calculate the normalized time series of air temperature, precipitation, surface soil moisture and scPDSI, and evaluate the dependency relationship of the high temperature-atmospheric dryness composite event and the severity of the composite event; Step S3: Response of regional extreme drought to high temperature and atmospheric dryness during the historical period; Combine the temperature, precipitation, surface soil moisture, and scPDSI normalized time series calculated in step S2 to assess the response of regional extreme drought to changes in high temperature and atmospheric dryness; Step S4: Temporal patterns and dependencies of high temperature and atmospheric dryness complex conditions under the background of anthropogenic climate change; combining the standardized time series of temperature and precipitation obtained in step S2, using the CMIP6 future scenarios, to estimate the probability of occurrence of different levels of high temperature and atmospheric dryness conditions in the future; Step S5: Estimating the recurrence probability of regional extreme drought events driven by high temperature and atmospheric dryness; calculating the additional impact of high temperature and atmospheric dryness on drought, clarifying the driving factors affecting regional extreme drought; based on the driving factors, quantifying the probability of historical and future regional extreme high temperature-atmospheric dryness-drought events exceeding the observed historical period every 10 years; Step S6: The spatiotemporal evolution characteristics and driving factors of the severity of the high temperature-atmospheric dryness complex event are analyzed. Combined with the precipitation and temperature data obtained in step S1, the Standardized Precipitation Index (SCI) and Standardized Temperature Index (STI) based on the original data and after removing the linear trend are calculated. The SCEI is then calculated based on these two indices to clarify the dominant factors affecting the historical changes in the SCEI. Step S7: Detecting climate change signals of high temperature-atmospheric dryness composite events; using a detection and attribution method based on rotational empirical orthogonal decomposition and an optimal fingerprint method to identify human activity signals of regional high temperature-atmospheric dryness composite events; Step S8: Estimate the severity of future high-temperature-atmospheric dryness combined events under different temperature rise levels and emission scenarios; combine the data obtained in step S1 to calculate the temperature rise level relative to the pre-industrial revolution, correspond the SCEI with the temperature rise level, and analyze the relationship between the severity of high-temperature-atmospheric dryness and temperature rise; combine the SCEI calculated for different future CMIP6 emission scenarios obtained in S1 and S2 to analyze whether controlling emissions can limit the increase in the severity of high-temperature-atmospheric dryness combined events.

2. The method for detecting climate change signals of drought events under high temperature and dry conditions according to claim 1, characterized in that: In step S1, the climate change data of drought events include: regional precipitation and temperature data based on observations and the self-calibrated Palmer Drought Index (scPDSI) and surface soil moisture data; temperature, precipitation, surface soil moisture, latent heat flux, sensible heat flux, relative humidity, wind speed, maximum temperature, minimum temperature data, as well as elevation and effective soil moisture data of the sixth phase of the International Coupled Comparison Program (CMIP6); and a single-model large sample set from the SPEAR dataset, a seamless system for prediction and Earth system research from the Geophysical Fluid Dynamics Laboratory, including precipitation and temperature data.

3. The method for detecting climate change signals of drought events under high temperature and dry conditions according to claim 2, characterized in that: Step S2 is specifically as follows: potential evapotranspiration is calculated using the Penman-Montes formula; the self-calibrated Palmer Drought Index (scPDSI) is calculated using potential evapotranspiration, precipitation, and effective soil moisture; precipitation, temperature, surface soil moisture, and the self-calibrated Palmer Drought Index (scPDSI) are standardized for horizontal comparison of different variables and different data sets; the probability of the combined occurrence of low precipitation and high temperature is calculated using the standardized data; the Copula function is used to calculate the probability multiplication factor for evaluating the dependency of the high temperature-atmospheric dryness composite event and the standardized composite event index (SCEI) for evaluating the severity of the composite event.

4. The method for detecting climate change signals of drought events under high temperature and dry conditions according to claim 3, characterized in that: Step S3 is specifically as follows: Combined with the temperature, precipitation, surface soil moisture, and scPDSI standardized time series calculated in step S2, the changes in the probability of regional extreme drought occurring simultaneously with high temperature, atmospheric dryness, and high temperature and atmospheric dryness over the past century and the past 20 years were compared to analyze the temporal evolution characteristics of drought, high temperature, and atmospheric dryness in historical periods. The coupled relationship between regional temperature and precipitation was constructed, and the changes in this coupled relationship over the past 20 years and the past 100 years were compared. The three-dimensional relationship between temperature, precipitation, and drought and its changes were constructed by combining scPDSI and surface soil moisture. The distribution of regional extreme drought in the two-dimensional temperature-precipitation coordinates at different periods was used to reflect the response of regional extreme drought to changes in high temperature-atmospheric dryness conditions.

5. The method for detecting climate change signals of drought events under high temperature and dry conditions according to claim 4, characterized in that: Step S4 specifically includes: combining the scPDSI normalized time series obtained in step S2, comparing the changing trends of different levels of high temperature-atmospheric dryness composite conditions in CMIP6 under the historical full forcing experiment and the natural forcing experiment only, and analyzing the role of anthropogenic climate change in the high temperature-atmospheric dryness composite conditions; The probability multiplication factor was calculated to analyze the changes in the interdependence between high temperature and atmospheric dryness in the future relative to historical periods; the probability of occurrence of different levels of high temperature-atmospheric dryness conditions in the future was estimated using the CMIP6 future scenario.

6. The method for detecting climate change signals of drought events under high temperature and dry conditions according to claim 5, characterized in that: Step S5 specifically comprises: calculating the additional impacts of high temperature and atmospheric dryness on drought, clarifying the driving factors affecting regional extreme drought; using the most extreme high temperature-atmospheric dryness-drought event observed in regional history as a standard, using the regional average values ​​of high temperature, atmospheric dryness, and drought for that event as an assessment threshold, and using model simulation data to calculate the historical and future occurrence probabilities of high temperature, atmospheric dryness, and high temperature-atmospheric dryness combined conditions under the assessment threshold; and calculating the changes in the probability of drought events of this level occurring in historical and future periods based on multi-model model simulation data. Assess how the average state of future precipitation and temperature will change relative to historical periods when extreme drought events occur, clarify how the dependence of regional extreme drought on high temperature and atmospheric dryness changes under high emission scenarios, and finally, quantify the probability of historical and future regional extreme high temperature-atmospheric dryness-drought events exceeding observed historical levels every 10 years.

7. The method for detecting climate change signals of drought events under high temperature and dry conditions according to claim 6, characterized in that: Step S6 is specifically as follows: combining the precipitation and temperature data obtained in step S1, respectively calculating the standardized precipitation index and the standardized temperature index based on the original data and the linear trend removed, and calculating the SCEI based on these two indices to quantify the temporal evolution trend of the severity of the high temperature-atmospheric dryness composite event based on the original data and the linear trend removed; The Pearson correlation coefficient of the SCEI, calculated based on the original data and the data with the linear trend removed, was calculated to determine whether the fluctuations in the SCEI were caused by long-term trends or precipitation-temperature coupling. A three-dimensional relationship between the standardized precipitation index, standardized temperature index, and SCEI was constructed, and the historical period was divided into three different stages to determine the response of the SCEI to temperature and precipitation in different periods. The multivariate linear regression method was used to quantify the contribution of changes in the regional standardized precipitation index and standardized temperature index to the changes in the SCEI, clarifying the dominant factors affecting the changes in the SCEI over the historical period.

8. The method for detecting climate change signals of drought events under high temperature and dry conditions according to claim 7, characterized in that: Step S7 is specifically as follows: S71: A detection and attribution method based on rotating empirical orthogonal decomposition (ROE) is first used. Three-dimensional SCEI data from the CMIP6 historical full forcing and future high-emission scenarios are selected. Rotating empirical orthogonal decomposition is then used to extract the spatial fingerprint of the dominant mode, i.e., the dominant empirical orthogonal function of the spatial mode with the maximum variance obtained through the spatiotemporal covariance matrix. The spatial fingerprint is then projected onto the observed SCEI and the pre-industrial experimental SCEI. The projection formula is as follows: Where Proj(x) represents the projection value of each year, F(i,j) is the dominant empirical orthogonal function, A(i,j) is the actual area of ​​each grid point, and SCEI(i,j,x) represents the observed or simulated SCEI value of each grid point at longitude i and latitude j; The trend value of the projected time series is then calculated. The trend values ​​of the observational data and the CMIP6 historical full forcing projection over L years are respectively used as the signal S(L), and the standard deviation of the trend distribution of the SCEI projection over L years of the CMIP6 pre-industrial revolution experiment is used as the noise N(L). The dimensionless signal-to-noise ratio SNR = S(L) / N(L) is calculated, where L is a preset value. If the SNR exceeds 1.64 or 2.57, it indicates that the signal is detected at the 90% and 99% confidence levels, respectively. Based on the model data, a continuously increasing L value is set. The calculated SNR is used to calculate the time of signal appearance, that is, the time when the forced signal begins to appear relative to the natural climate variability. S72: Detect and quantify the influence of external forcing on observed SCEI changes using the optimal fingerprint method; this method uses multiple linear regression and assumes that the observed changes are a response to external forcing and natural climate variability. The formula is as follows: Where y is the observed SCEI, X is the simulated SCEI under external forcing, is the uncertainty in the sampling of X, β is the scaling factor that adjusts the fingerprint to match the observation, and ε is the residual term associated with natural climate variability. The scaling factor is estimated using ordinary least squares and total least squares.

9. A storage medium, characterized in that: The storage medium stores instructions and data for implementing the method for detecting climate change signals of drought events under high temperature and dry conditions as described in any one of claims 1 to 8.

10. A device for detecting climate change signals of drought events under high temperature and dry conditions, characterized by: include: Processor and storage medium; the processor loads and executes instructions and data in the storage medium to implement the method for detecting climate change signals of drought events under high temperature and dry conditions as described in any one of claims 1 to 8.

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