Physical‐Background‐Constrained Bias Correction for Daily Precipitation Prediction Over China by a Subseasonal‐to‐Seasonal Model

Hindered by systematic bias and incapability in capturing the physical modulations of predictability sources, dynamical subseasonal‐to‐seasonal (S2S) models struggle to skillfully predict precipitation beyond 15 days. Based on the CMA‐CPSv3 model, this study introduced three revised QM correction methods (CMs) to improve prediction of daily precipitation in China on the subseasonal timescale. These QM methods comprised a sliding window approach (CM1) and a regional aggregation method (CM2)—both designed to increase sample sizes—and a background‐constrained method (CM3), incorporating the Indian Ocean Basin Mode (IOBM), designed to account for physical influences of potential predictability sources. Results revealed that the raw model bias, including overestimation of drizzle precipitation (≤ 1 mm/day), underestimation of medium to heavy rainfall (≥ 10 mm/day), and misrepresentation of the rain belt along the Yangtze River Basin were improved by the CMs, especially the regional aggregation‐based QM methods (CM2 and CM3). CM3 further improves prediction skill in capturing the spatial distribution pattern of precipitation and reducing the bias, especially under the positive IOBM phase. The superiority of CM3 stems from its ability to accurately capture the spatiotemporal influence of the IOBM on precipitation in China, demonstrating the effectiveness of physical‐background‐constrained bias CM on subseasonal predictions.