With population growth and socio-economic development, the use of fossil fuels not only impacts the environmental but also highlights its finite nature. Consequently, the quest for environmentally friendly and sustainable alternative energy solutions has become urgent. Offshore wind, as an emerging energy source, offers a continuous power supply for China. However, the instability of wind energy’s interannual variations can lead to insufficient energy supply for the wind power industry, emphasizing the importance of examining and predicting these variations.In this study, we employed the Time Series K-means clustering method to categorize winter interannual variations of wind energy along the Chinese coast into four regions: the North China Sea (NCS), East China Sea (ECS), Northern South China Sea (NSCS), and Southern South China Sea (SSCS). Subsequently, regression analysis was used to explore the relationship between interannual variations in regional wind energy and large-scale circulation anomalies. We found that interannual variations in NCS are related to the Arctic Oscillation (AO)-related cyclones (Anticyclones) in Northeast China, while those in ECS are associated with the central-type El Niño-Southern Oscillation (ENSO) and the Siberian High. Wind power in both SSCS and NSCS is influenced by the eastern-type ENSO-related Philippines cyclones (Anticyclones), with the north-south position of the continental high-pressure system also affecting their interannual variations; when the continental high-pressure system shifts northward (southward), NSCS (SSCS) is mainly affected.Considering the relatively high predictability of climate modes, we evaluated the predictive skill of wind energy along the Chinese coast using five climate models. Regarding climatology predictions, CMCC and JMA overestimate wind energy in the southern sea, contrasting with underestimations from NUIST and DWD. SEAS5 aligns closely with ERA5. Conversely, in the northern sea, all models except SEAS5 tend to overestimate wind energy. In terms of root mean square error (RMSE) in predictive skill, significant deviations are observed among various models for regions abundant in wind energy resources such as the Taiwan Strait, the Luzon Strait, and areas west of the Nansha Islands. CMCC exhibits the largest prediction error of wind energy resources in the Chinese Sea, while the SEAS5 model demonstrates the smallest prediction error. Concerning predictions of interannual variations, climate models show higher predictive skill for the wind energy index in the South China Sea, reflecting the models' strong predictive skill for ENSO. However, for northern regions, current climate models face challenges in predicting the influences of climate modes on wind power.This paper elucidates the relationship between the interannual variations of winter wind energy along the Chinese coast and large-scale circulation anomalies caused by climate factors. However, it does not delve into the underlying mechanism of this association from the perspective of atmospheric dynamics. Further investigation is needed to explore this intrinsic mechanism. Additionally, the interannual variations of wind energy in other seasons along the Chinese coast and their influencing factors also merit further exploration.