STBridge: A Spatio-Temporal Neural Bridge for Global Weather Forecasting

Medium-range weather forecasting requires preserving atmospheric structure over long lead times while controlling cross-scale error accumulation, maintaining spatial transport, and keeping meteorological variables coherently coupled. Existing models often rely on spherical graphs, multi-scale operators, temporal cascades, broad receptive fields, or variable-aware representations, but they typically model scale transfer, spatial transport, temporal evolution, and cross-variable interactions in separate pathways. To address these limitations, we propose STBridge, a Spatio-Temporal Neural Bridge that organizes global weather forecasting around a shared grid state. The model aggregates the encoded atmospheric history across multiple resolutions and returns the fused multi-grid state to the forecast grid, then learns directional spatial neighborhoods and temporal states for forecast evolution. The shared state supports spatial-semantic message exchange, followed by grid-wise coupling of the six atmospheric variables within each cell; a variableconditioned decoder produces the future fields. Experiments on the ERA5 dataset show that STBridge achieves the best average ACC and the lowest average RMSE among the compared foundation and baseline models, while ablation and visualization experiments confirm the effectiveness and accuracy of its core modules. These results demonstrate that a shared grid state can coordinate multi-scale context, spatio-temporal relations, spatial-semantic routing, and cross-variable coupling for global medium-range forecasting.