This study aims to analyze and compare the performance of two regional climate models (RegCM4.5 and WRF3.5) in simulating extreme rainfall over West Africa. We performed two simulations respectively at a spatial resolution of 50 km with the RegCM4.5 model at a spatial resolution of 12 km with the WRF3.5 model. These runs cover the period 1981-2010 and the driving fields (lateral boundary conditions) are from the Era-Interim reanalysis. The RegCM4.5 model simulates dry (wet) biases over the Sahel (Guinea Coast) while the WRF3.5 model simulates an opposite bias. This could be explained partly by the fact that the RegCM4.5 (WRF3.5) model underestimates (overestimates) the relative humidity and the monsoon flow over the Guinea Coast compared to the ERA-Interim reanalysis. Results also show that the spatial distribution and the annual cycle of rainfall over West Africa are well simulated by the two regional climate models despite the presence of some biases. The number of rainy days decreases from the southern to the northern Sahel for CHIRPS data and both regional climate models. All datasets show the highest rainfall intensities and the strongest values of the intense rainfall events over the Fouta Jallon highlands, Jos Plateau and Cameroun Mountains. The maxima of the mean 95th percentile of daily rainfall is located over the Guinea zone for CHIRPS datasets and both regional climate models. All datasets show a spatial distribution of the consecutive wet days similar to the number of rainy days with strong values over the orographic regions. When considering the consecutive dry days, all datasets exhibit strong values of this parameter north of 17.5°N (northern Sahel). The shorter consecutive dry days are observed over the area of the maximum precipitation (over the southern Sahel and the orographic regions). In terms of model biases, this study shows substantial differences between the two regional climate models used in this study suggesting the necessity to perform models intercomparison during the present-day before any choice for future projections.