Evaluating ecPoint and EMOS for Ensemble Post-processing of Precipitation Forecast

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 performance of these ensemble post-processing methods is conducted using the CMA 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 on 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.