Sources of Skill in Preseason Prediction of Atlantic Hurricane Activity: Forecast Timing, Model Capability, and Predictor Hierarchy

This study evaluates the 20-year operational performance (2006–2025) of a preseason prediction system for Atlantic hurricane activity developed at North Carolina State University (NCSU) and compares it with forecasts from Colorado State University (CSU), Tropical Storm Risk (TSR), and NOAA. Unlike previous studies based primarily on hindcast experiments, this analysis uses real-time forecasts generated under evolving model configurations, providing a realistic assessment of operational forecast skill. Results show that NCSU April forecasts exhibit lower mean absolute error than other April-issued forecasts and achieve performance comparable to later-issued forecasts from NOAA and CSU, indicating that improved model formulation can partially offset the advantage of later initialization. To identify the sources of forecast improvement, regression and ensemble analyses are conducted. Forecast adjustments between early- and late-season forecasts are primarily explained by changes in tropical North Atlantic sea surface temperature (SST), while ENSO contributes secondarily as forecast uncertainty decreases beyond the spring predictability barrier. These results establish a clear hierarchy of predictors, with Atlantic SST providing the dominant source of preseason predictability. Multi-model ensemble experiments further show that simple averaging does not outperform the best individual models; instead, selective combinations yield the highest skill, with optimal configurations differing between named storm and hurricane predictions, demonstrating that forecast improvement depends on combining complementary information rather than increasing ensemble size. Forecast performance is also shown to be predictand-dependent, with named storm counts more sensitive to late-spring environmental evolution and hurricane counts more strongly constrained by basin-scale thermodynamic conditions. Despite these advances, all models exhibit reduced skill during extreme seasons, reflecting the intrinsic limits of seasonal predictability. Overall, these results demonstrate that preseason hurricane forecast skill is governed by the interaction of forecast timing, model capability, and a hierarchical structure of environmental predictors, providing a unified framework for interpreting differences among forecasting systems and guiding future model development.