To enhance the risk management of wind farms under fluctuating wind resources, this study uses hourly wind speed observations from wind measurement towers at two wind farms in Hebei province as the reference to construct two types of wind power weather indices—Index Ⅰ (monthly mean wind speed) and Index Ⅱ (monthly available power generation)—based on hourly wind speed data from meteorological stations, MERRA-2, and ERA-5. Over the period from January 2022 to March 2023, basis risk analysis, pure premium rate calculation, and payout simulation are conducted. It is found that indices derived from meteorological station data exhibit the lowest basis risk across both wind farms. For tower 1 in particular, Index Ⅰ and Index Ⅱ yield correlation coefficients of 0.94 and 0.93, and basis risk volatilities of 0.042 and 0.047 GW·h, respectively—significantly outperforming indices based on reanalysis datasets. Moreover, Index Ⅱ consistently shows a higher payout-to-premium ratio than Index Ⅰ, indicating stronger capacity to identify and respond to low-wind events. Under the P75 (the probability that the index value exceeds this threshold is 75%; the same applies hereafter) trigger level, annual pure premium rates range from 7.11% to 8.00%, with more frequent and higher payouts; under the P90 level, rates fall to 2.80%—3.05%, with reduced coverage effectiveness. The wind power weather index insurance framework developed in this study demonstrates the feasibility and applicability of designing such insurance based on wind speed data, offering robust data and methodological support for site-level weather index insurance product development.

To address the strong weather dependence of the new energy industry and limitations of traditional insurance, it is imperative to accelerate the application of weather index insurance in new energy risk management. Based on a case study in the Beijiang river basin, this paper utilizes meteorological data from 1984 to 2023 and runoff data from 2003 to 2017 combined with hydrological and power generation potential models to analyze the spatiotemporal distribution characteristics of photovoltaic and hydrological climate resources on a monthly scale. Considering core processes such as trigger value setting, pure premium rate determination, compensation scheme formulation and verification, a basin-level photovoltaic and hydropower index insurance product is designed and applied. Furthermore, Kendall rank correlation analysis and standard deviation reduction percentage method are employed to systematically evaluate its hedging effects under different temporal-spatial conditions and different proportioning scenarios. The results show that the photovoltaic and hydropower index insurance, with monthly solar radiation and runoff as indices, can provide multi-level risk protection schemes by setting different monthly compensation trigger thresholds according to exceedance probabilities. Under different claim initiation standards, the annual average values of monthly pure premium rates for photovoltaic and hydropower index insurance are 7.1%—11.8% and 7.1%—39.9%, and the annual average values of monthly cumulative compensation ratios are 6.1%—13.3% and 6.9%—29.3% respectively. The spatial hedging performance of the photovoltaic and hydropower index insurance is superior to that of single-factor power generation index insurance. In the same spatial range, the complementarity of photovoltaic and hydropower generation potential is generally strong from June to October, showing a distribution pattern of "stronger in the southeast and weaker in the northwest". The risk diversification effect of photovoltaic and hydropower index insurance in different spatial areas is better than that in the same spatial area, and the diversification effect of "hydropower hedging solar power" is more optimal. In the scenario with the strongest hedging performance, when the proportion of photovoltaic and hydropower index insurance products is 7:3, the volatility of the insurance loss ratio reaches the smallest, which is respectively 19.2% and 51.9% lower than that of single photovoltaic and hydropower index insurance. Therefore, by optimizing the proportion of different insurance products, the volatility of the loss ratio of "hedging-type" insurance products can be effectively reduced, and their overall operational stability can be improved.

In the context of intensifying global climate change and frequent occurrences of extreme weather events, weather risk has emerged as a critical factor threatening the stability of the macroeconomy and the financial system. Weather derivatives mean financial derivatives based on weather indices (often referred to simply as "weather derivatives"), as innovative financial instruments for managing such risks, remain an undeveloped sector in China. This paper systematically reviews China's practical progress in policy support, multi-stakeholder collaboration, weather index development, and derivative product development and application. It elaborates on the significance of exploring weather derivatives to strengthen weather risk management in the real economy, unlock the value of meteorological data, and promote the interdisciplinary development of financial meteorology. Finally, the paper proposes future directions for research in financial meteorology, including prioritizing the needs of the real economy, integrating international experiences with national conditions, addressing fundamental market challenges, and establishing cross-departmental collaborative mechanisms. These recommendations aim to provide a reference for the exploration of weather derivative development pathways in China.

In the context of intensifying global climate change and the increasing frequency of extreme weather events, traditional insurance and government disaster relief systems are facing growing challenges. Catastrophe bonds (often referred to simply as "cat bonds"), which transfer catastrophe risks from insurance and reinsurance markets to capital markets, have emerged as an innovative financial tool. From the unique perspective of financial meteorology, this paper provides an in-depth analysis of the core supporting role of meteorological technology—particularly data collection, numerical forecasting, and artificial intelligence (AI) large-model technologies—throughout the entire lifecycle of catastrophe bonds, including product design, pricing, issuance, triggering, and settlement. This study outlines the overall landscape of the global catastrophe bond market and then focuses on the emerging development, policy-driven initiatives, and distinctive models in the Chinese market. Through comparative analysis, it identifies challenges China faces in terms of catastrophe risk data infrastructure, refined modeling, and market ecosystem. Finally, the paper proposes that future efforts should leverage frontier technologies such as AI large models, along with strategies such as constructing a national catastrophe risk data platform, deepening cross-sector integration of meteorology and finance, and enhancing the core capabilities of meteorological services, to provide solid scientific and technological support for establishing a multi-layered catastrophe risk dispersion system in China.

As one of the main tobacco cultivation regions, Southwest China features diverse terrain and experiences frequent hailstorms that seriously affect tobacco production, procurement, and the income of farmers. Hail suppression is an effective way to prevent and/or control hailstorms, yet most of its benefit assessment is based on comparisons between areas inside and outside the region of hail suppression. Objective and quantitative assessment is far less than sufficient. The present study constructs a hail yield loss model for tobacco. The hailstorm process on 1 July 2023 is taken as an example, and a detailed assessment of the economic benefits of hail suppression is conducted based on hail-related factors with/without hail suppression retrieved from radar observations and extrapolated data. Results are as follows. By comparing four regression models, an optimal relationship model is constructed between tobacco yield loss rate and hail maximum diameter, duration, and density. It is found that the log-linear regression model can effectively simulate tobacco crop damage caused by hails. Radar can reflect regional hail factors and tobacco hail damage at 1 km resolution. The yield loss areas and radar reflection areas of hails with larger diameters and longer durations are consistent. Using radar observations and extrapolated data, it is found that the hail suppression effect varies in different areas. The yield loss caused by hails can be reduced by up to more than 30% in nearly half of the hail suppression area. 1 Chinese Yuan (CNY) invested in the hail suppression operation during this hail process is able to bring about a tobacco loss reduction of 9.59 Chinese Yuan (CNY). This study provides a scientific basis for the research on methods of quantitative evaluation of economic benefits of hail suppression as well as financial benefits of meteorological services.

This study aims to design a weather index insurance product featuring dynamic payout throughout the entire growth cycle of crops. The product is intended to respond promptly to adverse weather conditions at different growth stages, provide rapid compensation, and convert insurance payouts into funds for disaster prevention and loss reduction. This approach supports and encourages farmers to take proactive measures in mitigating risks. Using county-level yield data of winter wheat from Hebei, Shandong, and Henan provinces (1990—2022) along with meteorological data, this study leverages the concept of residual learning from gradient boosting algorithms. The entire growth period of winter wheat is divided into five stages, and 16 meteorological indicators—such as temperature, precipitation, and wind speed are selected. A random forest-sequential method is employed to predict yield loss at each growth stage. Starting from the second stage, the residual from the previous stage's prediction is used as the target variable for modeling. Payouts are triggered whenever the predicted loss at any stage exceeds a predefined threshold. Multiple meteorological factors are found to influence winter wheat yield, with their impacts varying by growth stage and depending on the intensity and duration of weather conditions. Relying on a single indicator or a single growth stage is proved to be insufficient for accurately assessing final yield loss. The random forest-sequential method demonstrates strong out-of-sample predictive performance, effectively capturing the complex relationship between weather and yield. It outperforms both multiple linear regression and non-sequential random forest approaches, particularly in predicting large yield losses. When relatively severe yield losses occur, the proposed insurance product provides effective compensation. However, a certain degree of basis risk remains, especially in cases of minimal or no actual loss, where payouts could still be triggered. The algorithm based on sequential residual learning offers an effective means of predicting yield loss. By assessing risks stage by stage throughout the crop growth cycle, this method ensures timely payouts and enables early- to mid-season financial support for farmers, facilitating proactive disaster prevention and loss reduction.

In the context of global warming and frequent extreme weather events, temperature risk has become an increasingly prominent threat to economic and financial stability. Existing temperature models face limitations such as high computational demands, slow updates, and difficulty capturing extreme temperatures, making them inadequate for meeting the timeliness requirements of the economic and financial systems in interannual-scale risk quantification and stress testing. Based on datasets including the Yangtze river delta temperature index, surface observations, NCEP reanalysis, and CMIP6 model projections, this study proposes a computationally efficient temperature risk stress testing model. By improving the Ornstein-Uhlenbeck (O-U) model and incorporating key physical drivers using the LSTM (Long Short-Term Memory) method, the model enhances the description of extreme temperatures. Empirical analysis based on the Yangtze river delta temperature index from 2022 to 2024 demonstrates that the model effectively improves long-term prediction accuracy and extreme temperature simulation capability while maintaining low computational costs, with particularly strong performance in spring and summer. This model can serve as a flexible and efficient temperature risk stress testing tool for various sectors such as banking, insurance, and energy, supporting daily loss estimation and scenario simulation.