Skillful Probabilistic Prediction of Seasonal Extreme Precipitation Based on China Multi‐Model Ensemble Forecasts

ABSTRACT The skillful prediction of seasonal extreme precipitation anomalies is especially critical for agricultural planning, water resource allocation and flood mitigation. While single‐model deterministic dynamical systems are inherently limited in prediction skill and uncertainty quantification, the probabilistic predictions of multi‐model ensemble (MME) can effectively improve the prediction performance, integrate diverse physical parameterisations to generate comprehensive probability distributions and quantify climate uncertainty. To address this issue, this study systematically evaluates the probabilistic prediction skill of China Multi‐Model Ensemble (CMME) prediction system for summer extreme precipitation and explores the potential contributors to its advantages. The results indicate that the superiority of MME is more evident in the probabilistic prediction of seasonal extreme precipitation anomalies than in deterministic prediction. This advantage primarily stems from leveraging model diversity in physical processes to enhance prediction reliability, which is particularly evident in tropical regions where large‐scale forcing dominates. In contrast, for mid‐to‐high latitude regions, especially for East Asia, where internal variability is more significant, improving extreme event predictions crucially depends on increasing the ensemble size and the spread of the probability distribution. Furthermore, the optimal probability threshold for translating probabilistic forecasts into deterministic extreme event warnings is identified using the Heidke Skill Score (HSS) across the entire probability range. Based on this optimal threshold, the MME demonstrates stable prediction skill both globally and across key regions. This study confirms the advantages and explores the sources and contributors to the skill improvement of MME in probabilistic predictions of summer extreme precipitation, providing a robust scientific basis for the further objective extraction and utilisation of probabilistic information in seasonal extreme prediction.