A comparison of seasonal rainfall forecasts over Central America using dynamic and hybrid approaches from Copernicus Climate Change Service seasonal forecasting system and the North American Multimodel Ensemble

Seasonal rainfall forecasts provide information several months ahead to support decision making. These may use dynamic, statistical, or hybrid approaches, but their comparative value is not well understood over Central America. This study conducts a regional evaluation of seasonal focusing on two the leading dynamic climate ensembles: Copernicus Climate Change Service forecasting system (C3S) and North American Multimodel Ensemble (NMME). We compare multimodel ensemble mean individual model predictions key wet season periods in America better understand relative forecast skill at scale. Three types are compared: direct from C3S NMME ensembles, statistical approach using lagged observed sea surface temperature (SST), an indirect approach, driving with SST predictions. Results show that exhibit similar variability strong performance northern Pacific part weaker primarily eastern Nicaragua. In region, models have high across season. Indirect can outperform specific cases where lower predictive power (e.g., Nicaragua during early season). The generally reflects strength associations rainfall. based Tropical Atlantic SSTs best Niño3.4 late when each zone has stronger association Statistical competitive multiple cases, especially season, demonstrating how variety approaches enhance forecasting.