Predictive skill of North American Multi‐Model Ensemble seasonal forecasts for the climate rainfall over Central Africa

This study evaluates the predictive performance of North American Multi-model Ensemble (NMME) over Central Africa (CA) using historical rainfall data. The African Rainfall Climatology Version 2 (ARC2) is used as a substitute for reference observational data to examine capability 11 NMME and their ensemble mean (MME) in simulating rainfall. Using Kling-Gupta efficiency (KGE), Taylor skill score (TSS), Heidke score, evaluation models performed from lead 0 5 each season. results show that satisfactorily reproduce bimodal unimodal structure CA at level different seasons: December–February (DJF), March–May (MAM), June–August (JJA), September–November (SON). pattern correlation coefficient (PCC) shows values MME greater than ~0.69 TSS > 0.60 all four seasons. presents maximum DJF ~ 0.96 between 1 month time. With same time scale, just 85 % have KGE 0.42. It follows forecast increases, PCC model become small, with 0.12 JJA DJF, < 0.21 5. exhibit an important bias calculated scores quality decreases increasing time; this may justify constraint on keep good long-term seasonal CA.