AtmosWeave: Learning Atmospheric Dynamics for Global and Regional Weather Forecasting

Data-driven weather forecasting learns atmospheric evolution directly from large-scale historical data and has achieved strong forecasting performance. Despite this progress, existing machine learning models still struggle to capture local interactions across spatial hierarchies, preserve directional variation, and efficiently model long-range dependencies. We introduce AtmosWeave, a graph-based weather forecasting framework that combines multiscale mesh propagation with efficient grouped linear attention. Specifically, Scale-Decoupled Directional Mesh Propagation separates within-level normalization from cross-level fusion and incorporates directional deviations into local propagation, while Geography- and State-Aware Grouped Linear Attention enables selective long-range interactions through source grouping and target-dependent routing. For high-resolution regional forecasting, we further introduce CrossWarp to couple global and regional representations through learned feature warping and selective global information injection, enabling the regional model to better leverage global context. Experiments demonstrate that AtmosWeave achieves strong global forecasting performance while effectively leveraging global information for high-resolution regional forecasting. Code is included with the submission and will be released publicly upon acceptance.