Improving Medium‐Range Temperature Forecast Over the Tibetan Plateau Through Spatially Adaptive Fusion

Abstract Tibetan Plateau exerts profound impacts on the global weather system, whereas its medium‐range temperature forecast is challenging due to the complex topography. This study introduces Swin Transformer Fusion (STF), a spatially adaptive ensemble strategy that integrates forecasts from numerical weather prediction models (EC, GFS) and large meteorological models (Pangu, Fengwu). STF reduces 2‐m temperature (T2m) forecast bias by 29.14%–38.45% across 1–10 days lead, outperforming conventional multi‐model ensemble mean especially in high‐bias regions. Attribution analysis reveals that Fengwu contributes most to STF's output despite Pangu's superior accuracy, challenging the assumption that higher‐performing models should be weighed more heavily. These findings demonstrate that STF's spatially adaptive fusion enables region‐specific integration of model strengths, offering an effective ensemble forecast strategy for topographically complex regions.