Physics-Informed Teleconnection-Aware Transformer for Global Subseasonal-to-Seasonal Forecasting

Abstract: Subseasonal-to-seasonal (S2S) forecasting, which predicts climate conditions from several weeks to months in advance, presents significant challenges due to the chaotic dynamics of atmospheric systems and complex interactions across multiple scales. Current approaches often fail to explicitly model underlying physical processes and teleconnections that are crucial at S2S timescales. We introduce TelePiT, a novel deep learning architecture that enhances global S2S forecasting through integrated multi-scale physics and teleconnection awareness. Our approach consists of three key components: (1) Spherical Harmonic Embedding, which accurately encodes global atmospheric variables onto spherical geometry; (2) Multi-Scale Physics-Informed Neural ODE, which explicitly captures atmospheric physical processes across multiple learnable frequency bands; (3) Teleconnection-Aware Transformer, which models critical global climate interactions through tactfully injecting teleconnection patterns into the self-attention. Extensive experiments demonstrate that TelePiT significantly outperforms state-of-the-art data-driven baselines and operational numerical weather prediction systems, with remarkable improvements for atmospheric variables including a 57.7% reduction in RMSE for 2-meter temperature compared to previous best models.