Comparative Evaluation of ecPoint and Local EMOS for CMA-GEPS Precipitation Forecast over Eastern China

Post-processing of numerical weather prediction (NWP) models constitutes a pivotal link in enhancing forecast performance. Despite their recognition as cutting-edge point-based post-processing techniques, systematic comparative evaluations of ecPoint (ECWMF for point forecasts) and Ensemble Model Output Statistics (EMOS)—particularly assessments of their applicability outside Europe and to Chinese ensemble forecasting systems—remain insufficient. In this study, we evaluate two advanced post-processing techniques—Ensemble Model Output Statistics (EMOS) and the point-based European Centre for ECMWF statistical ensemble method (ecPoint)—for calibrating ensemble precipitation forecasts. A comprehensive assessment of the performance of these ensemble post-processing methods is conducted using the China Meteorological Administration’s Global Ensemble Forecasting System (CMA-GEPS) forecast over eastern China. The results demonstrate that both methods significantly reduce systematic biases and improve the reliability and dispersion of ensemble forecasts. Notably, improvement in forecast accuracy is observed even under convective weather conditions, and early warnings of extreme precipitation events. Overall, while both methods show comparable performance, they exhibit distinct behaviors across different regions. The ecPoint method slightly outperforms EMOS in terms of Continuous Ranked Probability Score (CRPS) and provides improved resolution and early warning capabilities at various precipitation thresholds.