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With climate change and intensification of the hydrological cycle, the stationarity of hydrological variables is becoming questionable, requiring appropriate flood assessment models. Frequency analysis is widely used for flood forecasting. This study aims to determine the most suitable models (stationary and non-stationary) for estimating the maximum flows observed at some stations spread across West Africa. A statistical analysis of the annual maximum flows in terms of homogeneity, stationarity, and independence was carried out through the Pettitt, modified Mann–Kendall, and Wald–Wolfowitz tests, respectively, to identify the stations whose flows are non-stationary. After that, the best-correlated climate covariates with the annual maximum flows of the non-stationary stations were determined. The covariates explored are the climatic indices of sea surface temperatures (SST). Finally, different non-stationary GEV models were derived by varying the scale and position parameters of the best-correlated index for each station. The results indicate that 56% of the annual maximum flow series are non-stationary. As per the Bayes information criterion (BIC) values, the performance of the non-stationary models (GEV, generalized extreme values) is largely greater than that of the stationary models. These good performances of non-stationary models using climatic indices open perspectives for the prediction of extreme flows in the study area.