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 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.