Skillful Kilometer-Scale Regional Weather Forecasting via Global and Regional Coupling

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 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.

Figures (17)

• Figure 1: Left: The Architecture of Global-Regional Weather Forecasting Model: Synoptic-scale context ($\mathcal{M}_{\mathrm{global}}$) drives mesoscale regional refinement ($\mathcal{M}_{\mathrm{regional}}$) via ScaleMixer, ensuring cross-scale coupling and consistency. Right: ScaleMixer Module: Bidirectional Cross-Scale Coupling via Key Position Identification and encoding. Key components include (1) key position identification, (2) coupling regional dynamics with global context via global-to-position and position-to-regional attention, and (3) global token adaptation incorporating regional features.
• Figure 2: ScaleMixer demonstrates superior deterministic forecasting skill compared to IFS-HRES at 0.05° resolution. Seven surface variables (T2M, U10, V10, Q, P, TCC, and SSRD) are evaluated using latitude-weighted RMSE (lower values indicate superior performance). (a) Hindcast results show ScaleMixer outperforms IFS-HRES across all variables during 2024/10–2024/12. (b) Operational forecasts confirm ScaleMixer maintains superiority performance (2025/01–2025/04).
• Figure 3: Left: Temporal evolution of 10m wind speed predictions initialized at 2024/10/30 12 UTC over the Hengduan Mountains (25.0–35.0°N, 95.0–105.0°E), China. Black arrows represent wind flow fields. ScaleMixer resolves enhanced resolution of orographic wind heterogeneity (peaking >10 m/s at crests and <2 m/s in valleys). Right: Corresponding temperature fields. Foehn effects are illustrated in the picture, characterized by 4–8°C leeward warming relative to windward slopes through adiabatic compression processes. ScaleMixer captures fine-grained temperature gradients, contrasting with IFS-HRES exhibiting spatial smoothing forecasts.
• Figure 4: Station distrbution map. The station observation dataset contains 2216 weather stations across China, which record hourly observations of meteorological variables such as temperature, air pressure, and wind speed.
• Figure 5: 2 metre temperature forecasts over China