SEAS5 skilfully predicts late wet-season precipitation in Central American Dry Corridor excelling in Costa Rica and Nicaragua - Kowal, KM; Slater, LJ; Van Loon, AF; Birkel, C (Wiley, 2022)
Better drought preparedness is critically needed in the Central American Dry Corridor (CADC). Seasonal forecasts can be used to build this preparedness but need localized evaluations to ensure they are relevant and useful. This study provides a CADC-focused assessment of the SEAS5 seasonal forecasting system produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). We evaluate SEAS5 predictions of the mean, variability, and extremes of precipitation across the CADC at 1–7-month lead times. We assess differences in regional forecast quality across seasons and lead times by evaluating spatial and temporal associations with El Niño–Southern Oscillation (ENSO) phase, topography, and continentality. Results show that SEAS5 precipitation forecasts often have better skill primarily during the mid to late wet season (July–October). In these months, low/normal precipitation forecasts outperform the climatological mean (1982–2016) up to 5- or 6-month lead times in some subregions. Forecast skill is often worse, however, for predicting precipitation during the early wet season, primarily in June. Forecast skill varies spatially across the region, with higher skill concentrated in the southeast (Costa Rica and Nicaragua). Forecast skill is significantly related to continentality and topography, and together these factors account for at least a quarter of the spatial variance in annual skill at all lead times. Forecast accuracy varies depending on ENSO phase: predictions are often worse in El Niño (warm ENSO) periods during the early wet season when ENSO also has a weaker association with cumulative precipitation relative to the later wet season. SEAS5 could be a particularly useful tool during the second half of the wet season in the southeast CADC, skilfully alerting of upcoming precipitation variability with over 3-month lead times.