Capability of Subseasonal-to-Seasonal Prediction Models in Forecasting Precipitation in South China During Rainy Seasons
XIE Jiehong
LIN Qiaomei
HU Yamin
LIN Jinhong
YE Mengxi
Abstract：This study evaluated the deterministic skills of subseasonal-to-seasonal(S2S)prediction models,including the CMA,ECMWF,NCEP,JMA,and UKMO models,in predicting precipitation during the rainy seasons in South China.It analyzed the predictability of various types of precipitation events utilizing the S2S prediction multi-model reforecast dataset and daily precipitation data from national meteorological stations.The results revealed limitations in these models' ability to predict precipitation intensity and variability.Generally,these models tended to overestimate precipitation intensity in the northwestern part of South China while underestimating it in the southeastern part.Moreover,these models underestimated the overall precipitation variability in the region.In terms of temporal correlation,most models showed useful skills,with correlation coefficients statistically significant at the 95％confidence level,in predicting precipitation for most areas during the rainy seasons,with a lead time of 1-2 pentads.However,most of these models exhibited low predictability in accurately capturing the anomalous precipitation patterns(measured by mean square skill),and their useful forecast lead time(mean square skill＞0)was only 1 pentad for most regions.Among the models,ECMWF demonstrated the highest level of prediction capability,exhibiting strong and consistent performance as indicated by the temporal correlation and mean square skill,with a predictability upper limit of 2 to 3 pentads.For forecasts with a short lead time(1 pentad),these models exhibited higher prediction skills for the second rainy season.However,as the lead time increased,the models' skills declined more rapidly during the second rainy season.Anomalous precipitation events generally exhibited higher predictability than average events at the S2S timescale.
Keywords：subseasonal-to-seasonal prediction modelsSouth Chinarainy seasonprecipitationprediction skill
Publication Date：2025-04-30
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：12( 224-235 )