In this study, we focused on describing the trends of Extreme Precipitation Indices (EPI) in Senegal and analyzing the significant links between their variability and key climatic factors such as the El Ni？o-Southern Oscillation Index (ONI), the Land-Ocean Temperature Index (LOTI), and the Land Surface Temperature Index (LST). Based on a century of daily rainfall data from various Senegalese stations, this study utilized twelve (12) EPIs calculated according to the definitions of the Expert Team on Climate Change Detection and Indices (ETCCDI). To analyze the temporal variation characteristics of extreme precipitation, the Mann-Kendall (MK) test was employed to perform a uniformity test on the precipitation data series. A dependence method through differentiation was used to remove data trends and observe correlations between the climate change indices ONI, LOTI, LST, and EPIs. An approach based on lagged correlations between the ONI index and the EPIs was applied to evaluate the predictability of extreme precipitation patterns in Senegal. Trend analysis indicates a significant decrease in total precipitation and frequency and intensity indices in most stations, while duration indices show no clear trend. Regarding their interannual variability, the analysis shows negative correlations between ONI and total precipitation, consistent with the known influence of ENSO on Sahel precipitation. Correlations with LOTI and LST indices, on the other hand, suggest that the Clausius-Clapeyron theory does not hold at Senegal’s latitudes, but that adjacent Atlantic ocean warming influence is crucial in modulating extreme precipitation patterns. Finally, on the predictability of extreme precipitation, the study shows a significant signal up to three months in advance with ENSO for 58% of the EPIs and up to two months in advance for 90% of the EPIs.
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