Assessment of ECMWF's Precipitation Forecasting Performance for China from 2017 to 2022

This study used the China Meteorological Administration (CMA) three-source fusion gridded precipitation analysis data as a reference to evaluate the precipitation forecast performance of the European Centre for Medium-Range Weather Forecasts (ECMWF) model for China from 2017 to 2022. The main conclusions are as follows. The precipitation forecast capability of the ECMWF model for China has gradually improved from 2017 to 2022. Various scores such as bias, equitable threat score (ETS), and Fractions Skill Score (FSS) showed improvements for different categories of precipitation. The bias of light rain forecasts overall adjusted towards smaller values, and the increase in forecast scores was greater in the warm season than in the cold season. The ETS for torrential rain more intense categories significantly increased, although there were large fluctuations in bias across different months. The model exhibited higher precipitation bias in most areas of North China, indicating overprediction, while it showed lower bias in South China, indicating underprediction. The ETSs indicate that the model performed better in forecasting precipitation in the northeastern part of China without the influence of climatic background conditions. Comparison of the differences between the first period and the second period of the forecast shows that the precipitation amplitude in the ECMWF forecast shifted from slight underestimation to overestimation compared to that of CMPAS05, reducing the likelihood of missing extreme precipitation events. The improvement in ETS is mainly due to the reduction in bias and false alarm rates and, more importantly, an increase in the hit rate. From 2017 to 2022, the area coverage error of model precipitation forecast relative to observations showed a decreasing trend at different scales, while the FSS showed an increasing trend, with the highest FSS observed in 2021. The ETS followed a parabolic trend with increasing neighborhood radius, with the better ETS neighborhood radius generally being larger for moderate rain and heavy rain compared with light rain and torrential rain events.