Global Subseasonal‐to‐Seasonal Marine Heatwave Forecasts Boosted by Explainable Deep Learning

Abstract

Marine heatwaves (MHWs) pose increasing risks to marine ecosystems and climate‐sensitive activities. Subseasonal‐to‐seasonal (S2S) MHW forecasts are essential for early warning, yet dynamical prediction systems show limited reliability. Here, we present MHWCorrNet, an interpretable deep learning‐based post‐processing framework for ECMWF IFS S2S MHW forecasts. It improves globally averaged MHW forecast skill by ∼9% at 1–6‐week lead times, with larger gains of ∼15% and ∼22% in the Northern and Southern Hemisphere extratropics. We show that regional background states systematically shape forecast correction strategies, revealing a previously overlooked aspect. In the extratropics, more variable air‐sea processes cause more missed events, so corrections primarily improve event detection. In the tropics, weaker thermal contrasts between MHW and non‐MHW conditions produce more marginal events near the detection threshold; therefore, skill gains mainly result from reduced false alarms. By leveraging interpretable diagnostics to quantify feature importance, MHWCorrNet adaptively modulates its reliance on key physical variables across latitude bands, placing stronger dependence on multiple predictors in extratropical regions to correct large residual errors, while exhibiting weaker dependence in the tropics, where sea surface temperatures variability is constrained by large‐scale climate modes and the correctable error space is smaller. This physically grounded adaptation enhances both forecast reliability and interpretability, underscoring the value of incorporating physical understanding into data‐driven prediction frameworks.