Abstract Severe precipitation in the Yangtze River Basin (YRB) poses escalating flood risks, underscoring urgent needs for skillful subseasonal prediction. In this study, we develop an integrated dynamical‐statistical downscaling model based on overlapping circulation‐precipitation co‐evolution (OCPCE), which merges prior and concurrent circulation evolution to predict rainfall anomalies. The core innovation shifts from conventional downscaling of dynamical model‐predicted circulation to an integrated framework combining observed recent evolution with highly predictable portions of future circulation from dynamical subseasonal‐to‐seasonal (S2S) models within an optimal overlapping time window. Implemented via evolution‐based singular value decomposition, this design maximizes retention of useful initial information while suppressing lower‐skill long‐lead predictions. The OCPCE model demonstrates statistically significant deterministic skill over YRB and reliable probabilistic predictions at 10–40‐day leads, substantially outperforming direct ECMWF‐S2S predictions. This work offers a physically coherent and operationally viable framework for improving subseasonal precipitation prediction, providing critical support for early flood warning and proactive disaster prevention.