Understanding the East China Winter 10 m Wind Speed Forecast Skill in the Copernicus <scp>C3S</scp> Multi‐Model Ensemble

ABSTRACT Accurate seasonal forecasting of wind energy resources is critical for optimising renewable energy integration. This study assesses the prediction skill of winter 10 m wind speed anomalies over East China using the Copernicus C3S Multi‐Model Ensemble forecasts, revealing that ECMWF and UK Met Office (UKMO) models demonstrate superior performance. We conduct comprehensive analyses of spatial anomaly correlations, temporal correlations, and empirical orthogonal function (EOF) decompositions by dividing the study area into northern (Zone 1), central (Zone 2) and southern (Zone 3) subregions. Results indicate strong forecast skill in Zone 1 and Zone 3, attributable to their tight correlation with well‐resolved large‐scale circulation patterns, whereas Zone 2 exhibited limited skill due to complex local influences. A hybrid dynamical‐statistical model is used to reconstruct EOF1, which is then combined with the original model outputs of EOF2 and EOF3. The result shows a substantial increase in temporal correlation coefficient in Zone 2. These findings establish that dynamical‐statistical integration effectively enhances regional wind predictions, offering actionable insights for grid operators and energy planners seeking to mitigate renewable integration challenges in evolving climate regimes.