A heat wave event evaluation method based on factorial experiment design

Abstract

The embodiment of the application discloses a heat wave event evaluation method based on factorial experiment design, comprising the following steps: S1, adopting a sliding window method to calculate a temperature threshold value of a research area; S2, acquiring temperature data of the research area, defining a heat wave event of the research area and quantifying heat wave characteristics of the research area according to the temperature data and the temperature threshold value, wherein the heat wave characteristics comprise frequency, duration, intensity, cumulative intensity, temperature rise rate and temperature drop rate; S3, using a multi-level factorial experiment design to analyze simulated values of the heat wave characteristics, so as to quantify the influence of different uncertainty factors on the heat wave characteristics. The heat wave event is defined and the heat wave characteristics are quantified in a set evaluation manner, errors caused by a single threshold value are avoided, and therefore, heat wave monitoring and evaluation results of the research area can be more accurately obtained.

Description

Technical Field

[0001] This application relates to the field of extreme weather and climate technology, and in particular to a method for assessing heat wave events based on factorial experimental design. Background Technology

[0002] A heat wave refers to a prolonged period of high temperatures that has destructive impacts on human health, agricultural production, and the ecological environment. Climate change, characterized primarily by rising temperatures, has exacerbated the frequency and intensity of heat waves, making the risk of heat-related disasters increasingly severe. To better address the negative impacts of heat wave events, it is necessary to monitor and assess their occurrence processes in order to develop appropriate disaster management measures.

[0003] Existing heatwave indicators lack uniformity, hindering comparisons of heatwave events across different regions and periods, and potentially failing to accurately identify heatwave processes within a warming context. Therefore, there is a need to develop heatwave indicator methods applicable to the context of global warming to more accurately monitor the occurrence of heatwave events. Summary of the Invention

[0004] Therefore, it is necessary to provide a heat wave event assessment method based on factorial experimental design that can accurately obtain heat wave monitoring and assessment results for the study area.

[0005] A method for assessing heatwave events based on factorial experimental design includes the following steps:

[0006] S1. The temperature threshold of the study area is calculated using the sliding window method;

[0007] S2. Obtain temperature data of the study area, define heat wave events in the study area and quantify heat wave characteristics of the study area based on the temperature data and the temperature threshold, the heat wave characteristics including frequency, duration, intensity, cumulative intensity, heating rate and cooling rate;

[0008] S3. A multi-level factorial experimental design is used to analyze the simulated values ​​of the heat wave characteristics in order to quantify the impact of different uncertainty factors on the heat wave characteristics.

[0009] The heat wave event assessment method based on factorial experimental design provided in this application defines heat wave events and quantifies heat wave characteristics by using ensemble evaluation, avoiding errors caused by single thresholds. This allows for more accurate heat wave monitoring and assessment results for the study area. Furthermore, the method employs multi-level factorial experimental design to analyze simulated values ​​of heat wave characteristics, calculating the impact of each factor and the interactions between factors on these simulated values. This quantifies the influence of different uncertainties on heat wave characteristics and allows for the selection of the factors and their levels that have the greatest impact on heat wave characteristics in the study area, providing more scientific and reasonable technical support for heat wave-related research. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart of a heat wave event assessment method based on factorial experimental design in one embodiment;

[0012] Figure 2 This is a line graph showing the simulated mean of heatwave characteristics from 1979 to 2021 under all combinations of different uncertainties in one embodiment.

[0013] Figure 3 This is a line graph showing the simulated mean values ​​of heat wave characteristics under different baseline periods in one embodiment;

[0014] Figure 4 This is a line graph showing the simulated mean values ​​of heat wave characteristics at different threshold levels in one embodiment.

[0015] Figure 5 This is a line graph showing the simulated mean of heatwave characteristics under different consecutive days in one embodiment. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0017] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0018] Furthermore, the use of terms such as "first" and "second" in this invention is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the term "and / or" throughout the text includes three solutions; taking A and / or B as an example, it includes technical solution A, technical solution B, and a technical solution that simultaneously satisfies A and B. Furthermore, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of a person skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0019] like Figure 1 As shown, this application provides a method for assessing heat wave events based on factorial experimental design, which includes the following steps:

[0020] S1. The temperature threshold of the study area is calculated using the sliding window method.

[0021] S2. Obtain temperature data for the study area. Based on the temperature data and temperature thresholds, define heat wave events in the study area and quantify the heat wave characteristics of the study area. Heat wave characteristics include frequency, duration, intensity, cumulative intensity, heating rate, and cooling rate.

[0022] S3. Multilevel factorial experimental design was used to analyze the simulated values ​​of heat wave characteristics in order to quantify the impact of different uncertainty factors on heat wave characteristics.

[0023] The heat wave event assessment method based on factorial experimental design provided in this application defines heat wave events and quantifies heat wave characteristics by using ensemble evaluation, avoiding errors caused by single thresholds. This allows for more accurate heat wave monitoring and assessment results for the study area. Furthermore, the method employs multi-level factorial experimental design to analyze simulated values ​​of heat wave characteristics, calculating the impact of each factor and the interactions between factors on these simulated values. This quantifies the influence of different uncertainties on heat wave characteristics and allows for the selection of the factors and their levels that have the greatest impact on heat wave characteristics in the study area, providing more scientific and reasonable technical support for heat wave-related research.

[0024] In this embodiment, a heat wave event refers to a high-temperature heat wave event that has occurred within the study time range of the study area; heat wave characteristics refer to parameters determined by analyzing the time parameters of each historical heat wave event to describe the occurrence of each historical heat wave event in the study area.

[0025] The statistical period is one year. Frequency refers to the sum of the number of days of all heat wave events in a year; duration refers to the longest duration of all heat wave events in a year; intensity refers to the largest temperature anomaly of all heat wave events in a year; cumulative intensity refers to the average of the sum of temperature anomalies of all heat wave events in a year; warming rate refers to the average daily temperature rise of all heat wave events in a year from the start to the peak temperature; and cooling rate refers to the average daily temperature drop of all heat wave events in a year from the peak temperature to the end.

[0026] In one embodiment, step S1 includes:

[0027] S11. Using a 30-year historical period as the baseline, the percentile threshold of daily temperature in the study area within the year was calculated using the 15-day window method.

[0028] The specific calculation formulas involved in step S11 are as follows:

[0029]

[0030] In the formula, Q represents the percentile threshold of the temperature on the i-th day of the year; T y,i T represents the daily temperature of the study area on the i-th day of the y-th year. y,i Specifically, it can be the daily maximum temperature T on the i-th day of year y. y,i It can also be the daily average temperature or daily minimum temperature on the i-th day of the y-th year; This represents the set of daily temperature data for the study area from year a to year b. This represents the set of daily temperature data within a 7-day window before and after date d; P represents the temperature at the corresponding percentile. P represents the cumulative percentile calculated by arranging all elements in the set of temperature data in ascending order and taking the temperature value of the element closest to the Nth percentile. The Nth percentile represents the Rth daily temperature being greater than the Nth percentile daily temperature. The specific calculation formula is as follows:

[0031]

[0032] In the formula, R total This indicates the total number of temperature data points.

[0033] Specifically, based on the actual needs of studying heat wave events in the study area, the start and end years 'a' and 'b' of the required historical time range for the study are determined, with the required historical time range being at least 30 years. A set of daily temperature data for years 'a' to 'b' in the study area is obtained. The date 'd' of the study object is determined, which is the date on which it needs to be determined whether it is a high-temperature day. The seven days before and after date 'd' are used as a window period, and a set of daily maximum temperature data for the window period determined by the seven days before and after date 'd' is obtained. for A subset of, according to the formula Q is confirmed.

[0034] In one embodiment, step S2 includes:

[0035] The Nth percentile of the daily temperature data in the study area is defined as the temperature threshold. Days with daily temperatures greater than the temperature threshold in the study area are defined as high-temperature days. Weather events with high-temperature days lasting for at least M days are defined as heat wave events, where N is 85, 90, or 95, and M is 3, 5, or 7.

[0036] Since dataset Q contains elements of daily temperature data, the elements in dataset Q are sorted from smallest to largest, and the corresponding cumulative percentiles are calculated. The temperature value of the element closest to the Nth percentile is defined as the temperature threshold. Therefore, the temperature threshold is a floating threshold. If the daily temperature on date d is greater than the temperature threshold determined for that day, then date d is defined as a high-temperature day. When three or more consecutive high-temperature days occur, it is recorded as a heat wave event.

[0037] Daily temperature includes the daily maximum temperature, daily average temperature, or daily minimum temperature, with temperature thresholds of 25°C, 30°C, 32°C, or 35°C. Specifically, the determination of a heat wave event is usually made by comparing the daily maximum temperature with the temperature threshold. In other embodiments, a heat wave event can also be defined by comparing the daily average temperature or daily minimum temperature with the temperature threshold.

[0038] Common methods for defining heatwave events include the absolute threshold method, the relative threshold method, and the empirical coefficient method. For example, the my country Meteorological Administration defines a heatwave event as a weather process in which the daily maximum temperature exceeds 35°C for three consecutive days; the World Meteorological Organization (WMO) recommends defining a heatwave event as a period in which the daily maximum temperature exceeds 32°C for more than three consecutive days; the Royal Netherlands Meteorological Institute defines a heatwave event as a period in which the daily maximum temperature exceeds 25°C for more than five consecutive days (with at least three days exceeding 30°C); some scholars define a heatwave event as a period in which the daily maximum temperature exceeds the 90th percentile of the 30-year historical baseline (1961-1990) for at least three consecutive days. This embodiment considers the influence of local historical climate and the tolerance of different populations to high temperatures in order to identify heatwave events more scientifically and reasonably.

[0039] In step S2, the specific calculation formulas involving the heat wave characteristics are as follows:

[0040]

[0041] HWD=t e -t s

[0042] HWImax =max(T) i -T clim,i )

[0043] HWI com =sum(T) i -T clim,i )

[0044]

[0045]

[0046] In the formula, HWF represents frequency; HWD represents duration; HWI max Indicates intensity; HWI com Indicates cumulative intensity; R onset Indicates the heating rate; R decline Indicates the cooling rate; D i This indicates the duration of each heat wave event within the year; t s and t e These represent the start and end times of the heatwave event, respectively; T i and T clim,i Let I represent the temperature and temperature threshold at time i, respectively; max This indicates the intensity of each heat wave event within the year; and They represent the t-th s Temperature and temperature threshold at time -1; t max This indicates the time when the heat wave is at its most intense.

[0047] It should be noted that the data sources for calculating heat wave characteristics can be different types of temperature data obtained from site observations or simulations by climate analysis models.

[0048] In one embodiment, step S3 includes:

[0049] S31. A three-factor, three-level experimental design was used to analyze the simulated values ​​of heat wave characteristics in order to quantify the impact of different uncertainty factors on heat wave characteristics. The three factors are the heat wave reference period, threshold level, and number of consecutive days. The three levels refer to the three different levels corresponding to each factor.

[0050] Specifically, for example, to define the temperature threshold level for a heat wave event, one can select the 85th, 90th, and 95th percentiles of the daily temperature data in the study area. A three-factor, three-level experimental design can be used to analyze the simulated values ​​of heat wave characteristics, calculate the influence of each factor and the interaction between factors on the simulated values ​​of heat wave characteristics, quantify the influence of different uncertainty factors on heat wave characteristics, and screen out the factors and factor levels that have the greatest impact on the heat wave characteristics of the study area.

[0051] In one embodiment, the specific formula involved in step S31 is as follows:

[0052]

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[0056]

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[0059]

[0060]

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[0063]

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[0065]

[0066] SS E =SS T -SS A -SS B -SS C -SS A×B -SS A×C -SS B×C -SS A×B×C

[0067] In the formula, y ijk The simulated value represents the characteristics of the heat wave, which can be any one of frequency, duration, intensity, cumulative intensity, warming rate, and cooling rate; μ represents the simulated mean of the heat wave characteristic under all combinations of different baseline periods, threshold levels, and consecutive days; α i This refers to the impact of the i-th base period (i = 1, 2, 3); β j It is the effect of the j-th threshold level (j=1,2,3); γ k This refers to the effect of the kth consecutive day (k = 1, 2, 3); (αβ) ijThis represents the interaction effect between the i-th baseline period and the j-th threshold level; (αγ) ik This represents the interaction effect between the i-th baseline period and the k-th consecutive day; (βγ) jk This represents the interaction effect between the j-th threshold level and the k-th consecutive day; (αβγ) ijk ε represents the overall interaction effect of the i-th baseline period, the j-th threshold level, and the k-th consecutive days; ε represents the random error.

[0068] E A E B E C E represents the main effects of the baseline period, threshold level, and number of consecutive days, respectively; A×B E A×C E B×C , representing the interaction effects between the base period and threshold level, the base period and consecutive days, and the threshold level and consecutive days, respectively; X represents the simulated mean of heat wave characteristics under all combinations of different base periods, threshold levels, and consecutive days; x(i,·,·) represents the simulated mean of heat wave characteristics under different base periods; x(·,j,·) represents the simulated mean of heat wave characteristics under different threshold levels; x(·,·,k) represents the simulated mean of heat wave characteristics under different consecutive days; x(i,j,·) represents the simulated mean of heat wave characteristics under different combinations of base periods and threshold levels; x(i,·,k) represents the simulated mean of heat wave characteristics under different combinations of base periods and consecutive days; x(·,j,k) represents the simulated mean of heat wave characteristics under different combinations of threshold levels and consecutive days.

[0069] SS A SS B SS C SS represents the sum of squares of simulated values ​​of heatwave characteristics under different baseline periods, threshold levels, and consecutive days; A×B SS A×C SS B×C SS represents the sum of squares of simulated values ​​of heatwave characteristics under different combinations of base period and threshold level, base period and consecutive days, and threshold level and consecutive days, respectively; A×B×C This represents the sum of squares of simulated values ​​of heatwave characteristics under different combinations of baseline period, threshold level, and number of consecutive days; SS T This represents the sum of squares of simulated values ​​of heatwave characteristics under all combinations of different baseline periods, threshold levels, and consecutive days; and SS. E This represents the sum of squared errors in the simulated values ​​of heatwave characteristics under all combinations of different baseline periods, threshold levels, and consecutive days; y i.. y .j. y ..ky represents the sum of simulated values ​​of all heatwave characteristics under the i-th baseline period, the j-th threshold level, and the k-th consecutive day, respectively; ij. y i.k y .jk y represents the sum of simulated values ​​of all heat wave characteristics under the combination of the i-th base period and the k-th consecutive days, the i-th base period and the k-th consecutive days, and the j-th threshold level and the k-th consecutive days, respectively; y… represents the sum of simulated values ​​of heat wave characteristics under all combinations of different base periods, threshold levels, and consecutive days.

[0070] To verify the statistical significance of uncertainties such as the baseline period (A), threshold level (B), and number of consecutive days (C), step S3 further includes:

[0071] S32, calculate the statistical significance of the interactions between each single factor and multiple factors. The calculation formulas involved in step S32 are as follows:

[0072]

[0073]

[0074]

[0075]

[0076]

[0077]

[0078]

[0079] In the formula, F A F B F C The F-test results represent the baseline period, threshold level, and number of consecutive days, respectively, and are used to test whether the single-factor baseline period, threshold level, and number of consecutive days have a significant impact on the simulated values ​​of heat wave characteristics; F A×B F A×C F B×C Fi represents the F-test results for the baseline period and threshold level, the baseline period and consecutive days, and the threshold level and consecutive days, respectively. These results are used to test whether the interactions between the baseline period and threshold level, the baseline period and consecutive days, and the threshold level and consecutive days have a significant impact on the simulated values ​​of heat wave characteristics. A×B×C The F-test results, representing the baseline period, threshold level, and number of consecutive days, are used to examine whether the interaction between the baseline period, threshold level, and number of consecutive days has a significant impact on the simulated values ​​of heat wave characteristics.

[0080] S33, using hypothesis testing based on the F-distribution to assess whether the effects of single-factor and multi-factor interactions are significant.

[0081] Specifically, if F A >F α，1，1 If factor A has a significant effect, then F A ≤F α，1，1 If the effect of factor A is not significant, then α can be 0.05. The method for determining whether the effects of single factors B and C, as well as the interactions of multiple factors A×B, A×C, B×C, and A×B×C, are significant is similar.

[0082] In one embodiment, the method further includes:

[0083] Step S4. Normalize the simulated values ​​of heat wave characteristics under different combinations of uncertainties to determine the uncertainty range of the simulation results. Normalizing the simulated values ​​of heat wave characteristics takes into account the influence of uncertainties such as the base period, threshold level, and number of consecutive days, avoiding errors that may be caused by a single heat wave definition. Simultaneously, the uncertainty range of the simulation results of heat wave characteristics can be obtained, thus enabling more accurate monitoring and assessment of heat wave events.

[0084] Specifically, as mentioned above, heat wave characteristics include frequency, duration, intensity, cumulative intensity, heating rate, and cooling rate. Normalization methods that can be used include the mean method, median method, and coefficient weighting method. The variance of the simulated values ​​of the heat wave characteristics under different combinations of uncertainties is calculated. The uncertainty interval of the simulation result of the heat wave characteristic is obtained by taking the simulated value ± variance.

[0085] The following describes a specific embodiment of the present invention. Through systematic description, the scientificity and rationality of the calculation results of the present invention are verified. It should be stated that the scope of protection of the present invention is not limited to the following embodiment. All other embodiments obtained by those skilled in the art without creative effort, involving the concepts and calculation methods mentioned in the present invention, are within the scope of protection of the present invention.

[0086] This embodiment calculates the heat wave characteristics of a certain region based on the daily maximum temperature, using data from 1979 to 2021. A three-factor, three-level experimental design is employed to analyze the simulated values ​​of the heat wave characteristics, quantifying the impact of different uncertainties on these characteristics.

[0087] Assuming the uncertainties affecting heat waves include a reference period (A), threshold levels (B), and consecutive days (C), with a 30-year base period, the reference periods for the three levels are defined as 1979-2008, 1985-2014, and 1992-2021, respectively. The percentile thresholds for daily temperatures within the year are calculated, with the threshold levels defined as the 85th, 90th, and 95th percentiles, respectively. The consecutive days for the three levels are defined as 3, 5, and 7 days, respectively. Based on these definitions, this embodiment considers a total of 3×3×3=27 heat wave simulation combinations.

[0088] Figure 2 This is a line graph showing the simulated mean of heatwave characteristics from 1979 to 2021 under all combinations of different uncertainties. These heatwave characteristics include frequency (HWF), duration (HWD), and intensity (HWI). max ), cumulative intensity (HWI) com ), heating rate (R) onset ) and cooling rate (R decline ), where the gray range represents the uncertainty interval of the simulation results of the heat wave characteristics (i.e., the simulation mean ± variance of the heat wave characteristics). Figure 3 , 4 Figures 5 and 6 are line graphs showing the simulated mean values ​​of heat wave characteristics under different baseline periods, threshold levels, and consecutive days, respectively. It can be seen that there are significant differences between different definitions of heat waves.

[0089] According to E A E B E C E A×B E A×C E B×C The calculation formula is used to calculate the main effects and interaction effects of different uncertainties. Taking heatwave frequency as an example, the main effects of three different baseline periods on heatwave frequency are 1.94, 0.07, and -2.02 days, respectively; the main effects of three different threshold levels on heatwave frequency are 5.18, -0.33, and -4.85 days, respectively; and the main effects of three different durations on heatwave frequency are 4.98, -1.02, and -3.96 days, respectively. The larger the main effect and interaction effect, the greater the impact of the uncertainty on the simulation results of heatwave characteristics, and the direction of the impact is shown. For example, the main effects of three different baseline periods on heatwave frequency are 1.94, 0.07, and -2.02 days, respectively, indicating that the heatwave frequency gradually decreases as the baseline period changes.

[0090] According to SS A SS B SS C SS A×B SS A×C SS B×CThe calculation formula is used to calculate the influence rate of different uncertainty factors, which represents the relative contribution rate of different uncertainty factors to the simulated value of heat wave characteristics. Taking heat wave frequency as an example, the influence rates of the baseline period, threshold level, and number of consecutive days are 8.6%, 48.9%, and 42.5%, respectively, indicating that the choice of threshold level and number of consecutive days has a greater impact on heat wave frequency.

[0091] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention's specification and drawings under the inventive concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A method for assessing heat wave events based on factorial experimental design, characterized in that, Includes the following steps: S1. The temperature threshold of the study area is calculated using the sliding window method; S2. Obtain temperature data of the study area, define heat wave events in the study area and quantify heat wave characteristics of the study area based on the temperature data and the temperature threshold, the heat wave characteristics including frequency, duration, intensity, cumulative intensity, heating rate and cooling rate; S3. A multi-level factorial experimental design is used to analyze the simulated values ​​of the heat wave characteristics in order to quantify the impact of different uncertainty factors on the heat wave characteristics; Step S2 includes: The Nth percentile of the daily temperature data in the study area is defined as the temperature threshold. Days in the study area with a daily temperature greater than the temperature threshold are defined as high-temperature days. Weather conditions where the high-temperature days last for at least M days are defined as the heat wave event, where N is 85, 90, or 95, and M is 3, 5, or 7. In step S2, the calculation formulas related to the heat wave characteristics are as follows: In the formula, Indicates frequency; Indicates duration; Indicates intensity; Indicates cumulative intensity; Indicates the rate of temperature increase; Indicates the cooling rate; D i This indicates the duration of each heat wave event within the year; and These represent the start and end times of the heatwave event, respectively. and Let I represent the temperature and temperature threshold at time i, respectively; max This indicates the intensity of each heat wave event within the year; and They represent the first Temperature and temperature threshold at any given time; This indicates the time when the heat wave is at its most intense.

2. The heat wave event assessment method based on factorial experimental design according to claim 1, characterized in that, Step S1 includes: S11. Using a 30-year historical period as the baseline, the percentile threshold of daily temperature in the study area within a year is calculated using the 15-day window method.

3. The heat wave event assessment method based on factorial experimental design according to claim 2, characterized in that, The specific calculation formulas involved in step S11 are as follows: In the formula, Q i The percentile threshold for the temperature on the i-th day of the year; This represents the daily temperature of the study area on the i-th day of the y-th year; This represents the set of daily temperature data for the study area from year a to year b. This represents the set of daily temperature data within a 7-day window before and after date d; P represents the set of all temperature data elements arranged in ascending order, with the corresponding cumulative percentiles calculated, and the temperature value of the element closest to the Nth percentile taken.

4. The heat wave event assessment method based on factorial experimental design according to claim 1, characterized in that, The daily temperature includes the daily maximum temperature, daily average temperature, or daily minimum temperature; and / or The temperature threshold is 25℃, 30℃, 32℃ or 35℃.

5. The heat wave event assessment method based on factorial experimental design according to claim 1, characterized in that, Step S3 includes: S31. A three-factor, three-level experimental design is used to analyze the simulated values ​​of the heat wave characteristics. The three factors are the heat wave reference period, the threshold level, and the number of consecutive days. The three levels refer to the three different levels corresponding to each of the factors.

6. The heat wave event assessment method based on factorial experimental design according to claim 5, characterized in that, The specific formulas involved in step S31 are as follows: In the formula, The simulated value represents the characteristics of a heat wave, which can be any one of frequency, duration, intensity, cumulative intensity, heating rate, and cooling rate. This represents the simulated mean of heatwave characteristics across all combinations of different baseline periods, threshold levels, and consecutive days. It is the impact of the i-th base period; It is the effect of the j-th threshold level; It is the effect of the kth consecutive day; This represents the interaction effect between the i-th baseline period and the j-th threshold level; This represents the interaction effect between the i-th baseline period and the k-th consecutive day. This represents the interaction effect between the j-th threshold level and the k-th consecutive day. ε represents the overall interaction effect of the i-th baseline period, the j-th threshold level, and the k-th consecutive days; ε represents the random error. , , These represent the main effects of the baseline period, threshold level, and number of consecutive days, respectively. , , These represent the interaction effects between the baseline period and the threshold level, the baseline period and the number of consecutive days, and the threshold level and the number of consecutive days, respectively. This represents the simulated mean of the heatwave characteristics under all combinations of different baseline periods, threshold levels, and consecutive days. This represents the simulated mean of heat wave characteristics under different baseline periods; This represents the simulated mean of heat wave characteristics at different threshold levels; This represents the simulated mean of the heatwave characteristics over different consecutive days; This represents the simulated mean of heatwave characteristics under different combinations of baseline periods and threshold levels; This represents the simulated mean of heatwave characteristics under different combinations of baseline periods and consecutive days; This represents the simulated mean of heatwave characteristics under different combinations of threshold levels and number of consecutive days; SS A SS B SS C SS represents the sum of squares of simulated values ​​of heatwave characteristics under different baseline periods, threshold levels, and consecutive days; A×B SS A×C SS B×C SS represents the sum of squares of simulated values ​​of heatwave characteristics under different combinations of base period and threshold level, base period and consecutive days, and threshold level and consecutive days, respectively; A×B×C This represents the sum of squares of simulated values ​​of heatwave characteristics under different combinations of baseline period, threshold level, and number of consecutive days; SS T This represents the sum of squares of simulated values ​​of heatwave characteristics under all combinations of different baseline periods, threshold levels, and consecutive days; and SS. E y represents the sum of squared errors of simulated values ​​of heatwave characteristics under all combinations of different baseline periods, threshold levels, and consecutive days; i.. y .j. y ..k y represents the sum of simulated values ​​of all heatwave characteristics under the i-th baseline period, the j-th threshold level, and the k-th consecutive day, respectively; ij. y i.k y .jk Let y represent the sum of simulated values ​​of all heatwave characteristics under the combination of the i-th baseline period and the k-th consecutive days, the i-th baseline period and the k-th consecutive days, and the j-th threshold level and the k-th consecutive days, respectively; … This represents the sum of simulated values ​​of heatwave characteristics under all combinations of different baseline periods, threshold levels, and consecutive days.

7. The heat wave event assessment method based on factorial experimental design according to claim 6, characterized in that, Step S3 also includes: S32, calculate the statistical significance of the interactions between each single factor and multiple factors. The calculation formulas involved in step S32 are as follows: In the formula, , , The F-test results represent the baseline period, threshold level, and number of consecutive days, respectively. , , The F-test results are represented for the baseline period and threshold level, the baseline period and consecutive days, and the threshold level and consecutive days, respectively. The results of the F-test are presented for the baseline period, threshold level, and number of consecutive days. S33, using hypothesis testing based on the F-distribution to assess whether the effects of single-factor and multi-factor interactions are significant.

8. The heat wave event assessment method based on factorial experimental design according to claim 1, characterized in that, Also includes: S4. Normalize the simulated values ​​of heat wave characteristics under different combinations of uncertain factors to determine the uncertainty range of the simulation results of heat wave characteristics.

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