Since the signing of the Paris Agreement in 2015, the global energy structure has undergone unprecedented adjustment, and renewable energy has ushered in a new period of development opportunities. From the perspective of energy stability and sustainable development, this paper uses the generalized autoregression-conditional heteroscedasticity mixed data sampling model (GARCH-MIDAS) to explore the predictive power of climate policy uncertainty (CPU) on the index volatility of renewable energy. At the same time, eight uncertainty indices, including the economic policy uncertainty index and geopolitical risk index variable, are introduced to discuss the impact on the volatility of renewable energy. Furthermore, the out-of-sample prediction accuracy of each model is tested by the out-of-sample ROS2, Model Confidence Set (MCS), direction-of-change (DoC) and other evaluation methods. Climate policy exhibits a superior ability to predict renewable energy volatility, offers a new perspective for the accurate prediction of renewable energy volatility, and provides a reliable guarantee for the sustainable development of the energy market and financial market. •Climate policy uncertainty (CPU) has a significant negative impact on long-term volatility of Renewable Energy.•CPU has an excellent out of sample prediction effect on Renewable Energy volatility.•The prediction performance of CPU was better than that of the other eight exogenous variables.•The predictability of the CPU is robust by a set of robustness tests