Based on an accurate assessment of the risks of agricultural production, together with the information required for insurance policies, the reasonable design and evaluation of insurance products is an important guarantee for the sustainable development of China's agricultural insurance policies. According to historical meteorological data as well as rice growth stage and yield data, based on the construction of meteorological index for rice high temperature disaster insurance, combined with comprehensive regionalization and assessment of insurance risk in Jiangsu province, by setting different deductible amounts, insurance pure rates were set and corresponding products were designed. In addition, a comprehensive evaluation of the effectiveness of the application of the rice heat damage meteorological index insurance product has been achieved by using three methods: index evaluation, historical retrospective analysis, and analysis of typical heat damage events. The study found that under different deductible amounts for yield reduction rates, the determined pure insurance rates for rice high-temperature heat damage in various counties in Jiangsu province exhibited a distribution pattern of "high in the southwest and low in the northeast". The deductibles were ultimately determined to be 2.5% for low-risk, 2.5% for medium-risk and 7.5% for high-risk areas, after taking into account the incentives for farmers to participate and the level of coverage. The corresponding pure insurance rates were determined to be 5.09%, 5.27% and 5.26% respectively. The designation of this index insurance product was relatively reasonable from the point of view of the company's operational efficiency, loss compensation pressure, level of risk transfer and coverage of the farmer's production. In low-risk, medium-risk, and high-risk areas, the operational sustainability indices of insurance companies were 14%, 37%, and 76%, respectively, while the stability indices of farmer production security were 6%, 9%, and 21%, respectively, and the average compensation rates for high-temperature heat damage events were 111%, 122% and 303%, respectively. In typical high-temperature heat damage years, the average compensation amount in Jiangsu province exceeded 1800 yuan·ha⁻¹, and the number of compensation counties accounted for more than 50%. The results of this study may serve as references for the evaluation of the operational characteristics of meteorological index insurance products, as well as for the adjustment of insurance product design schemes, and the promotion and implementation of insurance products. 

 In order to solve the difficulties in the design of solar greenhouse crop weather index insurance products, such as the different effects of indoor meteorological conditions on crops due to the differences in insulation materials and building structure parameters, and the basis risk caused by the same rate caused by regional meteorological disaster risks, this paper designed a weather index insurance product based on the distribution of cucumber low-temperature chilling injury risk in Tianjin solar greenhouse. Based on the relationship between the microclimate factors in the greenhouse and the outdoor weather conditions in the greenhouse, based on the theory of natural disaster risk assessment, a comprehensive risk assessment model of low-temperature chilling injury of cucumber was constructed and the risk zones of low-temperature chilling injury of cucumber were divided into different risk zones. The meteorological claims index was calculated by combining the disaster index for different grades and the yield reduction rate of cucumbers, and the insurance rates for different risk zones were determined based on the regional coefficients of the risk of hypothermia injuries. The insurance premiums and compensation amounts per unit area for cucumber hypothermia injuries under different claim indices were calculated, and the weather index insurance product for cucumber hypothermia injuries in solar greenhouses was designed. The results showed that the high-risk areas of cucumber low-temperature chilling injury in solar greenhouses in Tianjin were mainly located in the northern part of Tianjin, including most of Baodi, the southwest of Jizhou district, the central part of Wuqing district and Beichen district, the northwest of Hangu in Binhai new area and the southeast of Ninghe district. The gross rates of cucumber low-temperature chilling injury high, medium and low risk areas were 8.05%, 7.21% and 6.27% respectively, combined with the actual physical and chemical cost of greenhouse production, the compensation amount per unit area of cucumber was set at 45000 yuan·ha⁻¹, and a typical low-temperature process in Tianjin in November 2015 was taken as an example to introduce the claim of cucumber low-temperature chilling weather index insurance, the research and development of the product effectively solved the problem that Tianjin's current facility agricultural insurance only protects facilities such as sheds and insulation materials without protecting crops.

By utilizing annual precipitation and forage yield per unit area data in Guoluo prefecture from 2003 to 2017, the drought weather index (DI_q) was constructed by selecting the percentage of precipitation anomaly (PA), the yield loss rate (YLR_q) was calculated by employing the Hodrick prescott filter (HP filter) method to estimate the trend yield of pastures, the correlation between the drought index and the pasture yield loss rate was fitted using a fixed-effects model, finally, to establish a loss rate expectation value model. Enabling the design of forage drought weather index insurance product and facilitating the determination of the pure insurance premium rate. The results clearly demonstrated that: (1) the risk of drought-related disasters affecting forage in Guoluo prefecture had a spatial distribution characteristic, increasing progressively from southeast to northwest. Specifically, with Maduo county being high-risk area that required focused attention on drought prevention. (2) From 2003 to 2017, the average forage yield per unit area in the six counties of Guoluo prefecture showed a fluctuating upward trend. Banma county had the highest average yield, averaging an impressive 11126.67kg·ha⁻¹ over the 15 years period. (3) There was a significant （P<0.050） linear relationship between the drought index (DI_q) and the forage yield loss rate (YLR_q), expressed by the equation YLR_q=0.311DI_q+0.035（P<0.050）. Consequently, each unit increase in the DI_q resulted in a 0.311 percentage points increase in the forage YLR_q. When the DI_q was 0, other factors lead to a 3.500% reduction in forage yield. (4) Using the EasyFit software to analyze the probability distribution models of the drought index, four distinct distribution functions were identified for the forage drought index across in the six counties of Guoluo prefecture: Generalized Pareto, Error, Johnson SB, and Generalized Extreme Value models. The Anderson−Darling (A−D) test statistics confirmed the goodness of fit for all models, the A−D test statistics were all less than 0.3. The probability of encountering mild, moderate, severe, and extreme drought conditions in each county of Guoluo prefecture were found to range between 8.750% and 30.350%, 13.000% and 34.530%, 0 and 13.790%, and 0 and 0.820%, respectively. (5) With 100% guarantee level provided by the forage drought weather index insurance product in Guoluo prefecture, the pure premium rates for the forage drought weather index insurance product in the six counties of Guoluo prefecture ranged from 2.259%−3.748%, with the highest pure rate of 3.748% in Maduo county in the northwest and the relatively lowest rate of 2.259% in Dari county. The spatial distribution pattern of pure premium rates for pasture drought weather index insurance gradually increased from southeast to northwest. Based on the spatial distribution of pastures and drought conditions in Guoluo prefecture, it was recommended that counties with higher pure premium rates, such as Maduo and Maqin, be considered as pilot counties to implement differentiated fiscal subsidy policies for insurance premiums.

Taking the wine grapes from Fangshan district, Beijing as the research object, based on the wine grapes' phenological and quality indicators sample data from wineries and long-term meteorological data, the analytic hierarchy process, stepwise regression, multiple regression were used to establish wine grape quality evaluation index and wine grape quality meteorological index. The quality meteorological index insurance product for the wine grape in Fangshan district was designed by the probability optimal statistical method to calculate the distribution of the quality meteorological index and the parameter method to determine the pure insurance premium rate. Results showed that: (1) the weights of total sugar content, sugar-to-acid ratio, and pH value to the quality of wine grapes in Fangshan district were 0.643, 0.283, and 0.074, respectively. From 0.099 to 0.905, the wine grape quality evaluation index provided a good representation of quality. (2) the key meteorological factors affecting the quality of wine grapes are the average daily maximum temperature in the 20 days before harvest, total precipitation throughout the entire growth period, average relative humidity during the pigmentation period, and daily average precipitation during the pigmentation period. With a correlation coefficient of 100.38 (P<0.001), the quality meteorological index for wine grapes established based on stepwise regression is consistent with the quality loss rate of wine grapes. (3) Johnson SB distribution was the optimal distribution model for the quality meteorological index of wine grapes in Fangshan district. The pure premium rate for the quality meteorological index of wine grape insurance product was 11.75%. The break-even problem caused by climate change can be addressed by setting concession or rate adjustment coefficients based on the insurer's actual needs and risk estimates.

The development of offshore wind power in China is at risk of generating less power due to low wind speeds, resulting in losses for operating and investors. Based on ERA5 wind field data, this study first assesses fluctuation risk in China's offshore wind resources, and then designs an offshore wind power generation index insurance product. Results showed that: (1) the wind resources for offshore China were most abundant in the Taiwan Strait. It was more abundant in the open areas of the East China Sea and the South China Sea than in the Bohai Bay and the Beibu Gulf. Wind resources were more plentiful in the autumn and winter than in the spring and summer. (2) There was an increasing trend in the occurrence of small offshore wind resource events over time. The fluctuation rate was highest in the south of the East China Sea and the Taiwan Strait. It gradually decreased from the open sea to the land, and gradually increased from west to east in the northern part of the South China Sea. The intensity of the fluctuations was greater on the western side of the islands in the offshore region. (3) The risk of downward fluctuation in power generation was highest in the Taiwan Strait, followed by the northeastern South China Sea and the Beibu Gulf. The Yellow Sea, the northern and southern parts of East China Sea, and the northwestern part of South China Sea were at the lowest at risk. (4) The offshore wind power generation index insurance used actual monthly power generation as the index, effectively capturing the monthly differences in wind resources. It offered insurance plans with different levels of risk protection by setting different payout trigger thresholds based on the probability of default. The pricing accounts for the additional insurance cost arising from managing the risk of low−frequency, high−loss events, as well as regional risk variations. The corresponding premium rates were 7.045%, 7.384% and 8.685% when the monthly payout trigger thresholds were set at 95%, 93% and 90% of the exceedance probability level, respectively. This insurance can stabilize wind farm revenues by compensating losses and improving the revenue floor, effectively managing the risk of wind resource variability.

Developing actuarially fair premium rates is essential for the sustainable and efficient advancement of area yield insurance. Incorporating climate forecast information is one of the important means to enhance the accuracy of premium calculation in area yield insurance pricing. This study introduced the Standardized Precipitation Evapotranspiration Index (SPEI) into the pure premium rate making method of the U.S. Department of Agriculture's Risk Management Agency (RMA) to construct an SPEI−RMA model. The model was applied to calculate pure premium rates for corn area yield insurance in Heilongjiang, Jilin, and Liaoning provinces of Northeast China. An out-of-sample game framework was employed to compare the performance of the SPEI−RMA method with the traditional RMA method in insurance pricing, aiming to provide a scientific basis for designing more precise area yield insurance products. Results indicated that when fitting the trend of crop yield, compared to the RMA method, the SPEI−RMA method increased the coefficient of determination (R²) by 0.044, 0.088, and 0.153 and reduced the mean squared error (MSE) by 0.068, 0.067, and 0.213 for Heilongjiang, Jilin, and Liaoning, respectively, thereby enhancing model estimation accuracy. The median absolute differences in pure premiums between the two methods are 0.105, 0.114, and 0.087 for the three provinces. Out-of-sample game result revealed that at coverage levels of 70%, 80%, and 90%, the SPEI−RMA method more accurately determines pure premium rates, yielding significant economic and statistical benefits. 

This study aims to investigate the impact of inversion layer on insurance products for tea cold-frost damage index, to optimize the design of weather index-based tea insurance products and the selection of reference weather stations for insurance claims and to help tea growers better understand the science behind temperature inversion. Sounding data from Hangzhou weather station and ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) in the spring (February to April) of the period from 2008 to 2019 were used to analyze the spatiotemporal distribution of inversion layer and the basis risk due to altitude difference. The results showed that current weather stations were not dense enough to represent the overall weather risk of the insured area. Also, basis risk existed even if the reference weather stations were close to the insured area. This was because basis risk was altitude dependent and could be affected by the inversion layer. It was shown that the inversion layer was thicker and more intense in February than in March and April. As a result, it should be more careful when selecting the reference weather stations for early spring tea grown at different altitudes to minimize the basis risk. Moreover, the weather index-based yield loss could be deviated from the actual yield loss. The variation of the basis risk in the vertical direction was larger for tea plantations located in the mountains than in the plains. Therefore, it is recommended to treat altitude as a key variable when designing weather index-based insurance products in order to reduce the basis risk.

 The lack of separate statistics on wind and rain damage by disaster-causing factors in the typhoon disaster data in Guangdong is a contributing factor to the existence of typhoon catastrophe index insurance basis risk. To mitigate this basis risk, this paper proposes a method to separate typhoon wind and rain damage. An analysis was conducted on the typhoon disaster data from 2014 to 2022 for 36 counties (districts) in nine coastal cities in Guangdong, focusing on wind and rain indicators. A multiple regression method was employed to construct a typhoon wind and rain disaster loss separation model, which separated the disaster loss due to wind and rain in the typhoon disaster statistics of each county (district). Furthermore, the typhoon observation data and direct economic loss data from Jiangmen Taishan city and Xiangqiao district of Chaozhou were presented as case studies to illustrate the model's validation. Linear correlation analysis showed that eliminating or reducing the effect of multicollinearity on the regression equation at the beginning of modeling may result in the loss of some valid information. The Gamma distribution was found to appropriately fit the distribution pattern of typhoon wind, rain, and damage in Guangdong. The results indicated that 22 counties (districts) passed the significance test at the 0.05 level. The model successfully separated wind- and rain- related damage in Guangdong's typhoon disaster loss statistics, providing a valuable reference for categorizing and analyzing typhoon disaster data by cause.

In order to transfer and reduce the drought risk in the Weibei region, and ensure economic income and food production security for farmers, meteorological data, spring maize yield and growth period from 1990 to 2020 for 23 counties of the Weibei region were used in this study. The model between drought index and yield reduction rate was established in the specific growth period. Then the spring maize drought weather index insurance product in the Weibei region was designed and the pricing research was carried out. Therefore, the insurance rate was adjusted by using the drought risk assessment results in case to reduce the basis risk. The results showed that: (1) the seedling-milk ripe stage was the key period of water demand for spring maize in the Weibei region. And there was a significant linear relationship between drought index (I) and yield reduction rate (D) during this growth period: D=0.383I−3.959 (P<0.05). (2) The precipitation during the seedling-milk ripe period decreased from northwest to southeast while the drought risk increased from northwest to southeast. And the drought risk in southeast was higher than that in the northwest. (3) Based on the drought yield reduction rate model and the insurance regulations, 40% of the drought index was taken as the compensation trigger value. Then the different drought index compensation standards were determined, and the adjusted insurance rates were between 6.9%−8.9% and the premiums were 375−480 Yuan per hectare based on the drought risk assessment results. This study can provide a scientific basis for the Weibei spring maize weather index insurance products designation, premium pricing, and basis risk effectively reduction. 

Weather index insurance has become one of the important methodsfor agricultural weather risk transfer due to its transparent information and convenient claims process. In this study, Chenxi county in Hunan wasselectedas an example, based on the daily minimum temperature data and Camellia oleifera(C.oleifera) production data from 2009 to 2020,the extreme minimum temperature (T_c) and the negative effective accumulated temperature below 0℃(TT) were selected during the flowering and young fruit formation period of C.oleifera (from November to April of the next year)to determine the freezing injury index of C.oleifera and construct the index-disaster model. The Anderson-Darling test was used to obtain the optimal probability distribution model for the risk of C.oleifera freezing injury, and to determine the pure premium rate and compensation standard corresponding to different yield reduction rates. The actual compensation data from China Pacific Property Insurance Co.,Ltd. Hunan Branch (CPIC) in February 2022 was used for validation. The results showed that during the flowering and young fruit formation period of C. oleifera, TT had a higher correlation with the yield reduction rate than T_c, and was used to characterize the meteorological index for freezing injury to C.oleifera in Chenxi county. The risk distribution of C.oleifera freezing injury conformed to the 3-parameter logarithmic logistic distribution, with the position parameter of 2.537, scaleparameter of 0.323, and threshold parameter of −1.805. When the compensation trigger condition TT was 2.1-20.8℃·d, the net premium rate was determined to be 0.85%-7.85%, and the compensation standard corresponding to the production reduction rate at all levels was 3750-48750yuan·ha⁻¹. After calculation, the average compensation amount for the major C.oleifera producing towns in Chenxi county in February 2022 (5000yuan·ha⁻¹) was basically consistent with the actual compensation amount of CPIC (4954yuan·ha⁻¹). Based on the influence of freezing injury during the flowering and young fruit formation periodon the yield of C.oleifera in Hunan, a weather index insurance scheme for C.oleifera freezing injuries has been designed, which can serve as a reference for Hunan to establish and improve its insurance system for C.oleifera.

 In order to cope with climate change, adapt to urban development and reduce the risk of urban meteorological disasters, based on the insurance claim data caused by meteorological disaster and surface meteorological observation data of four districts in Shijiazhuang city from 2008 to 2018, statistical methods were used to analyze the change characteristics and disaster-causing factor of urban insurance claim quantity caused by heavy rain. The simulated model for claim quantity due to heavy rain was constructed by considering the disaster-causing factor as an independent variable and the claim quantity as a dependent variable. The feasibility of the model was tested by using F−test, ANOVA and actual cases from 2019 to 2021. Finally, risk level thresholds of insurance claim caused by heavy rain were divided based on the key meteorological factor. The results were as follows: heavy rain was the main disaster that led to weather-related insurance claims of four districts in Shijiazhuang city, and the claim quantity due to heavy rain showed a significant increase trend. Five simulated models of claims quantity due to heavy rain were developed using either one or multiple linear regression analysis methods. After testing, it was determined that the maximum rainfall intensity was the key factor affecting the claim quantity of four districts in Shijiazhuang city. Using the maximum rainfall intensity as risk index, the risk of disaster claims due to heavy rain was divided into four levels: mild, moderate, severe and extremely severe. On this basis, through the development of heavy rain risk monitoring system for urban insurance industry in the later stage, it can essentially meet the risk monitoring and early warning needs of the industry, and provide technical support for promoting the risk reduction service of "prevention is more important than compensation" in the insurance industry. 

 In order to improve the rationale for determining the premium rate for weather index insurance for tea frost damage in Anhui province. In this paper, authors refined and revised the meteorological index for tea frost damage in Huangshan based on historical disaster data. The ANUSPLIN interpolation software was used to establish the spatial interpolation model of the meteorological indices. Based on historical meteorological data from Huangshan, a refined risk assessment of tea frost damage in Huangshan was conducted with the help of a geographic information system. Taking Huangshan Maofeng tea as the research object, the risk assessment results and tea economic output were comprehensively considered to determine the insurance district and insurance period of tea frost damage, and the weather index trigger value and premium rate during some period were also designed considering daily tea frost risk and actual needs of agricultural insurance operation, and the insurance pure premium rates of tea frost damage at different periods and altitudes were calculated. The results showed as follows: the extreme minimum temperature (T_d) 3°C was used as the threshold value of tea frost damage index, according to the interval of 1℃, the tea frost damage index were divided into 2℃<T_d≤3℃, 1℃<T_d≤2℃, 0<T_d≤1℃, −1℃<T_d≤0, −2℃<T_d≤−1℃, −3℃<T_d≤−2℃ and T_d≤−3℃ 7 disaster grades, the corresponding average loss rates of tea buds were 5%, 15%, 25%, 35%, 50%, 70% and 90% respectively. The distribution of frost damage risk in Huangshan had an obvious regional characteristics, with higher the altitudes having a higher risk of frost damage, and the time series showing a decreasing trend with time. The insurance region of the weather index of tea frost damage of Huangshan Maofeng was 400−1000m above sea level, and the insurable period was from March 16 to April 15, when the extreme minimum temperature reached 2℃ to trigger the compensation. The insurance pure premium rates at 400−500m, 500−600m, 600−700m, 700−800m, 800−900m and 900−1000m above sea level were 1.4%, 1.9%, 2.7%, 3.7%, 4.9% and 6.4% respectively. The insurance pure premium rates for the four periods of coverage from March 16 to March 30, March 21 to April 4, March 26 to April 9, March 31 to April 15 were 1.2%−5.0%, 0.9%−3.8%, 0.6%−2.2% and 0.3%−1.5% respectively. The insurance-only premium rate for tea frost damage in Huangshan Maofeng shows an increasing trend with altitude and a decreasing trend with time delay.