Comparison of Daily Precipitation Bias Correction Methods Based on Four Regional Climate Model Outputs in Ouémé Basin, Benin

Abstract

Precipitation projections from regional climate models in West Africa are attributed with significant biases with respect to the observed. This study aims at evaluating of six methods of precipitation bias correction on four RCM (CCLM, CRCM, RACMO and REMO) outputs in the Ouémé basin. The bias correction methods used are classified into three namely: the Delta approach, the Linear Scaling method and the quantile approaches. Corrected and uncorrected RCM precipitation data were compared with the observed using Mean Absolute Error (MAE) and Root Mean Square error (RMSE). The findings showed that raw outputs of regional climate models (RCMs) are characterized with several biases. In general, the models overestimate precipitation. For daily precipitation correction, the quantile approaches assuming a gamma distribution for daily precipitation were not able to reduce the biases of precipitation. The empirical quantile mapping and the adjusted quantile mapping are the most effective in correcting the biases of daily precipitation. Thus the adjusted quantile mapping can be used to correct biases of precipitation projections for modeling the future availability of water resources.