Sources of skillful seasonal forecasts of springtime CONUS tornado activity

Despite progress in continental United States (CONUS) tornado forecasting, operational seasonal outlooks remain challenging due to intrinsic predictability limits. While previous studies link the El Niño–Southern Oscillation (ENSO) to spring tornado frequency, relying on single tropical indices misses crucial regional variability. Here, we present a novel hybrid dynamical-statistical framework that introduces a paradigm for long-lead predictability. Utilizing retrospective forecasts from NOAA/GFDL’s state-of-the-art SPEAR model, we demonstrate for the first time that a strategic selection of global sea surface temperature modes yields highly useful, skillful predictions with lead times approaching 4 months. Dominant predictable patterns are rooted in transitioning ENSO phases, the Pacific Meridional Mode, and the trend-associated Caribbean High. Unlike conventional predictors, our framework filters internal noise and captures high-wavenumber anomalies. These findings carry substantial implications for climate services, bridging the gap between weather forecasting and seasonal risk management.