EAAC-S2S: East Asian Atmospheric Circulation S2S Forecasting with a Deep Learning Model Considering Multi-Sphere Coupling

Subseasonal-to-seasonal (S2S) forecasting for East Asian atmospheric circulation poses significant challenges for conventional numerical weather prediction (NWP) models. Recently, deep learning (DL) models have demonstrated significant potential in further enhancing S2S forecasts beyond the capabilities of NWP models. However, most current DL-based S2S forecasting models largely overlook the role of global predictors from multiple spheres, such as ocean, land, and atmosphere domains, that are crucial for effective S2S forecasting. In this study, we introduce EAAC-S2S, a tailored DL model for S2S forecasting of East Asian atmospheric circulation. EAAC-S2S employs the cross-attention mechanism to couple atmospheric circulations over East Asia with representative multi-sphere (i.e., atmosphere, land, and ocean) variables, providing pentad-averaged circulation forecasts up to 12 pentads ahead throughout all seasons. Experimental results demonstrate, on the S2S time scale, that EAAC-S2S consistently outperforms the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System by decreasing the root-mean-square error (RMSE) by 3.8% and increasing the anomaly correlation coefficient (ACC) by 8.6%, averaged across all 17 predictands. Our system also shows good skill for examples of heatwaves and the South China Sea Subtropical High Intensity Index (SCSSHII). Moreover, quantitative interpretability analysis including multi-sphere attribution and attention visualization are conducted for the first time in a DL S2S model, where the traced predictability aligns well with prior meteorological knowledge. We hope that our results have the potential to advance research in data-driven S2S forecasting.