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∘ (∼5km ) and 1-hour resolution over China, significantly outperforming operational NWP and AI baselines on both gridded reanalysis data and real-time weather station observations. It exhibits exceptional skill in capturing fine-grained phenomena such as orographic wind patterns and Foehn warming, demonstrating effective global-scale coherence with high-resolution fidelity. The code is available at https://anonymous.4open.science/r/ScaleMixer-6B66.
@article{arxiv.2603.28173,
title = {Skillful Kilometer-Scale Regional Weather Forecasting via Global and Regional Coupling},
author = {Weiqi Chen and Wenwei Wang and Qilong Yuan and Lefei Shen and Bingqing Peng and Jiawei Chen and Bo Wu and Liang Sun},
journal= {arXiv preprint arXiv:2603.28173},
year = {2026}
}