Both the Frequency Matching Method (FMM) and cluster analysis are widely recognized as standard bias correction techniques for precipitation forecasts. Therefore, how to effectively integrate cluster analysis with FMM to improve precipitation forecasting skills emerges as a critical scientific issue. To improve the forecasting skill of heavy rainfall, this study leverages forecasts from the European Centre for Medium-Range Weather Forecasts Global Ensemble Prediction System during the summers from 2020 to 2024. In this study, ablation experiments are designed based on the clustering results of historical precipitation characteristics and a FMM approach that anchors frequency. The Clustering-Ensemble sampling-anchoring frequency FMM (Cluster-FMM in short) significantly improves the (probability) forecasting skill of heavy rainfall events. Notably, the 100 mm Fractions Skill Score increases by 30%—80%. Two extreme precipitation cases ("23·7" in North China and "24·7" in Hunan Province) show that the Cluster-FMM results effectively correct the dry biases in ensemble forecasts of heavy precipitation. The ablation experiments also confirm that the clustering analysis, ensemble sampling and anchoring frequency collectively contribute to the enhancement of forecasting accuracy for heavy precipitation events. For operational systems, the Cluster-FMM provides a new perspective for improving accuracy of ensemble precipitation forecasts.

Ensemble prediction play a vital role in numerical weather prediction. Hence, how to effectively extract information from ensemble members to improve deterministic precipitation forecasting skills has always been a challenging issue. Based on precipitation data from the CMA-GEPS (China Meteorological Administration Global Ensemble Prediction System), a stepwise correction method based on Segmented Hierarchical Clustering (SHC) for daily precipitation is proposed. To evaluate the effect of the SHC method, two comparative experiments are further verified in this study based on TS score and ETS score. Effects of SHC are compared with that of an Ensemble Mean (EM) forecast method and a directed Hierarchical Clustering (HC) method. Results indicate that the deterministic forecast by the proposed SHC method can improve the predictability of heavy precipitation. Taking more probabilistic forecasting via the segmented correction scheme in correction method, SHC performs better than EM and HC. Meanwhile, SHC shows a better feasibility based on long-term forecast verification during the summertime in 2021; it also has much better effects on extreme precipitation event such as the case of heavy rainfall in Zhengzhou on 20 July 2021. For operational system, the developed SHC method provides a new tool that can further improve the result of ensemble forecasting.