Subseasonal Precipitation Prediction in the Yangtze River Delta Region Using an Adaptive Bias Correction Method

Abstract. Accurate and reliable prediction of subseasonal precipitation is crucial for disaster prevention and mitigation, particularly in the densely populated and economically developed Yangtze River Delta (YRD) region. Despite substantial progress in subseasonal-to-seasonal prediction (S2S) since the launch of the World Meteorological Organization’s S2S program, global models still exhibit considerable biases in the YRD region. To address these systematic errors in 3 to 6-week precipitation forecasts, this study employs an Adaptive Bias Correction (ABC) machine learning correction method to post-process biweekly accumulated precipitation predictions from the Climate Forecast System version 2 (CFSv2) and European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (ECMWF) models. Evaluation against the 2021 CLDAS high-resolution gridded dataset reveals substantial improvements after ABC correction. For the CFSv2 model, the root mean square error (RMSE) is reduced by 3.79 mm (5.5 %) for weeks 3–4 and by 8.32 mm (12.6 %) for weeks 5–6. The ECMWF model shows an even larger RMSE reduction of 10.26 mm (15 %) across weeks 3–6, with wintertime errors decreasing by up to 35.7 %. Spatial pattern correlation and overall forecast skill are also markedly enhanced. For high-impact low-temperature rain/snow and freezing rain events, the ABC method reduces absolute bias in the CFSv2 model by 21.1 %–33.3 % and in the ECMWF model by 10.4 %–20.5 %, effectively eliminating the pronounced wet bias over the Hunan–Jiangxi–Fujian area. These improvements provide critical technical support for operational early warning of freezing rain disasters in the YRD region.