Abstract: In the context of increasing global climate change and frequent extreme weather events, the stable operation and development of the electricity market face significant risks. As an important component of weather derivatives, weather futures can transform meteorological data into tradable financial products, achieving market-based allocation of weather risks. They provide efficient risk-hedging tools for power generation companies, electricity retailers, and large consumers, helping to optimize resource allocation. This paper takes Shanxi province as an example and electricity and meteorological data from 2022 to 2024 are used in the study. By integrating electricity market indicators and meteorological factors, a model is constructed using multiple linear regression and nonlinear transformations. A stepwise regression approach is applied to select a concise and significant set of features, which are then used to form the Daily High Temperature Index (DHTI). Corresponding weather derivative contracts are designed accordingly. The constructed index incorporates meteorological elements such as the daily temperature index as well as electricity factors including day-ahead price, real-time price, and renewable energy output. After nonlinear transformation and dimensionless processing, the index demonstrates a strong correlation with electricity revenue and exhibits good model stability. The case study demonstrates that this index can be effectively used for hedging by both power generators and consumers, smoothing revenue fluctuations caused by temperature changes. It provides low-cost and high-efficiency risk management tools for electricity market participants, promoting the improvement and development of electricity market in China.