云南省极端降水多模式可预报性评估OA

Extreme precipitation often leads to severe casualties and economic losses,making advancements in its prediction critically important.Given the central role of numerical models in modern forecasting operations,a deep understanding of their performance is fundamental to improving forecast accuracy.This study employs the root mean square error(RMSE),spatial threat score(TS),and spatial correlation coefficient(COR)to evaluate the predictability of 21 extreme precipitation events in Yunnan from 2014 to 2024,using ensemble forecasts from the China Meteorological Administration(CMA),the National Centers for Environmental Prediction(NCEP),and the European Centre for Medium-Range Weather Forecasts(ECMWF)within the TIGGE(THORPEX Inter-active Grand Global Ensemble)dataset.Overall predictability ranked as NCEP>ECMWF>CMA.The 90th and 75th percentile ensemble forecasts(ES90 and ES75)showed high reference value across models.ES75 dem-onstrated better skill for precipitation intensity,while ES90 performed better in spatial precipitation distribution,with ECMWF>NCEP>CMA in both cases.ES75 also exhibited superior spatial consistency with observations.For precipitation area forecasting,the most skillful combinations were:ECMWF-ES75 for moderate rain,ECMWF-ES90 for heavy rain,NCEP-ES90 for torrential rain,and CMA-ES90 for severe torrential rain.Sea-sonal variation was evident:ECMWF showed the highest predictability in winter,and NCEP in other seasons.Predictability also varied by influencing weather system-ECMWF performed best for pre-trough precipitation,while NCEP was superior for other system types.Model predictability for extreme precipitation primarily de-pended on the forecast skill of influencing systems.System spatial extent and location determined rainband pre-dictability,while system intensity and moisture transport governed precipitation magnitude predictability.Mod-els performed better for precipitation influenced by large-scale systems than by smaller-scale systems.