Title: Skillful Kilometer-Scale Regional Weather Forecasting via Global and Regional Coupling
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: Weather forecasting, Regional weather forecasting, downscaling, Deep neural networks

Abstract: Data-driven weather models have advanced global medium-range forecasting, yet high-resolution regional prediction remains challenging due to unresolved multiscale interactions between large-scale dynamics and small-scale processes such as terrain-induced circulations and coastal effects. This paper presents a **global-regional coupling framework** for kilometer-scale regional weather forecasting that synergistically couples a pretrained Transformer-based global model with a high-resolution regional network via a novel bidirectional coupling module, **ScaleMixer**. ScaleMixer dynamically identifies meteorologically critical regions through adaptive key-position sampling and enables cross-scale feature interaction through dedicated attention mechanisms. The framework produces forecasts at  $0.05^\circ$ ($\sim 5 \mathrm{km}$ ) and 1-hour resolution over China, significantly outperforming operational NWP and AI baselines. It exhibits exceptional skill in capturing fine-grained phenomena such as orographic wind patterns, Foehn warming, and coastal transitions during typhoon events, demonstrating effective global-scale coherence with high-resolution fidelity. The code is available at https://anonymous.4open.science/r/ScaleMixer-6B66.