%0 Journal Article
%A Diakhaté, Moussa
%A Suárez Moreno, Roberto
%A Gómara Cardalliaguet, Íñigo
%A Mohino Harris, Elsa
%T Statistical-observational analysis of skillful oceanic predictors of heavy daily precipitation events in the Sahel
%D 2020
%@ 2073-4433
%U https://hdl.handle.net/20.500.14352/6537
%X In this paper, the sea surface temperature (SST) based statistical seasonal forecast model (S4CAST) is utilized to examine the spatial and temporal prediction skill of Sahel heavy and extreme daily precipitation events. As in previous studies, S4CAST points out the Mediterranean Sea and El Niño Southern Oscillation (ENSO) as the main drivers of Sahel heavy/extreme daily rainfall variability at interannual timescales (period 1982–2015). Overall, the Mediterranean Sea emerges as a seasonal short-term predictor of heavy daily rainfall (1 month in advance), while ENSO returns a longer forecast window (up to 3 months in advance). Regarding the spatial skill, the response of heavy daily rainfall to the Mediterranean SST forcing is significant over a widespread area of the Sahel. Contrastingly, with the ENSO forcing, the response is only significant over the southernmost Sahel area. These differences can be attributed to the distinct physical mechanisms mediating the analyzed SST-rainfall teleconnections. This paper provides fundamental elements to develop an operational statistical-seasonal forecasting system of Sahel heavy and extreme daily precipitation events.
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