Reconstruction and Projection of Near‐Surface Wind Speed in China Based on Transformer Neural Network

Accurate projection of wind speed is essential for assessing wind energy resources and strategically planning future energy development. However, current Global Climate Models (GCMs) display discrepancies between simulated and observed near‐surface wind speed (NSWS), which limits the precise evaluation of wind energy potential. Inspired by the principles of transfer learning, this paper employs a neural network model, referred to as WindNet, to project changes in NSWS across China. WindNet learns the characteristics of NSWS through self‐supervised pretraining and is subsequently fine‐tuned to function as an NSWS reconstruction model. The results show that the WindNet model effectively captures the spatiotemporal features of NSWS compared to GCMs outputs, particularly in high‐altitude regions of China, thereby enhancing spatial resolution and prediction accuracy. The root‐mean‐square error (RMSE) was reduced to 0.5 m · s −1 , with a Pearson correlation coefficient of 0.94. Considering future changes in NSWS under various Shared Socioeconomic Pathway (SSP) scenarios, projections indicate a declining trend in NSWS across China from 2026 to 2100, with the effects of different emission scenarios on NSWS distribution proving to be minimal. These results hold substantial scientific significance and practical utility for evaluating long‐term changes in wind energy resources.