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ABSTRACT: This study explores the predictive skill of seasonal rainfall characteristics for the first rainy (and planting) season, May–June, in Central America. Statistical predictive models were built using a Model Output Statistics (MOS) technique based on canonical correlation analysis, in which variables that forecast with the Climate Forecast System version 2 (CFSv2) were used as candidate predictors for the observed total precipitation, frequency of rainy days and mean number of extremely dry and wet events in the season. CFSv2 initializations from February to April were explored. The CFSv2 variables used in the study consist of rainfall, as in a typical MOS technique, and a combination of low‐level winds and convective available potential energy (CAPE), a blend that has been previously shown to be a good predictor for convective activity. The highest predictive skill was found for the seasonal frequency of rainy days, followed by the mean frequency of dry events. In terms of candidate predictors, the zonal transport of CAPE (uCAPE) at 925 hPa offers higher skill across Central America than rainfall, which is attributed in part to the high model uncertainties associated with precipitation in the region. As expected, dynamical model predictors initialized in February provide lower skill than those initialized later. Nonetheless, the skill is comparable for March and April initializations. These results suggest that the National Meteorological and Hydrological Services in Central America, and the Central American Regional Climate Outlook Forum, can produce earlier more skilful forecasts for May–June rainfall characteristics than previously stated. [ABSTRACT FROM AUTHOR]