Seasonal precipitation forecast over Mexico based on a hybrid statistical–dynamical approach

Abstract

We introduce a new hybrid scheme for seasonal precipitation prediction over the Mexican territory. The scheme takes as input sea surface temperature (SST) predicted from the ECMWF System 4 coupled model (Sys4) identifying three predictors, namely SST over the western Pacific (WP), eastern Pacific (EP) and tropical Atlantic (TA); these predictors are used to forecast precipitation on a high‐resolution grid via a multiple regression model. Comparison of the performance of the hybrid scheme with the Sys4 model for ensemble re‐forecasts for the period 1982–2010 shows that the hybrid model adds skill to the Sys4 forecast, with higher correlations and Heidke skill scores throughout most of the Mexican territory, both in winter and summer. In winter the contribution of the EP is dominant in modulating precipitation via the effects of El Niño‐Southern Oscillation (ENSO), while in summer the contributions of the WP and TA are dominant in different Mexican regions. The hybrid scheme, applied to high‐resolution precipitation observations, also produces significant fine‐scale variability of forecast skill and mechanism contributions. Overall, we show that our hybrid approach can provide a useful tool to improve seasonal precipitation forecasts over Mexico.

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