Subseasonal 2-m Temperature Prediction over East Asia Based on SwinUNet-AR

This study focuses on subseasonal 2-m temperature prediction over East Asia for lead times of 1–6 weeks. Based on ERA5 reanalysis data from January 1982 to December 2024, multiple variables, including temperature, wind fields, and geopotential height, were selected as predictor fields. A SwinUNet-AR subseasonal prediction model integrating SwinUNet, AFNO, and Resize-Conv was constructed. The results indicate that the integrated architecture supports multiscale spatial representation, long-range dependency modeling, and high-resolution spatial reconstruction. Among different prediction strategies, simultaneous multi-lead prediction showed the most stable performance during Weeks 3–6. Multivariable input produced a statistically significant improvement in cumulative ACC during Weeks 3–6. The cumulative ACC increased by 0.07 compared with the scheme using only T2m as input, corresponding to an improvement of approximately 12.1%. The ablation experiments further indicate that the combined use of AFNO and Resize-Conv yields a modest improvement in medium- and extended-range forecast skill, particularly in cumulative ACC. Compared with the baseline SwinUNet, SwinUNet-AR reduced the mean RMSE by 0.02 °C during Weeks 3–4 and 0.03 °C during Weeks 5–6, while increasing the cumulative ACC during Weeks 3–6 by 0.04, corresponding to an improvement of approximately 7.4%. Compared with raw CFSv2 forecasts, SwinUNet-AR reduced the mean RMSE over Weeks 1–6 by 0.84 °C, corresponding to a reduction of approximately 44.9%. During Weeks 4–6, the mean RMSE was approximately 0.83 °C lower, corresponding to a reduction of about 40.8%. In terms of ACC, SwinUNet-AR achieved values comparable to those of CFSv2 during Weeks 4–6 and slightly higher values at some lead times. Overall, SwinUNet-AR shows potential for improving medium- and extended-range subseasonal 2-m temperature prediction over East Asia and provides a useful data-driven framework for regional subseasonal forecasting.