Statistical bias correction of regional climate model simulations for climate change projection in the Jemma sub-basin, upper Blue Nile Basin of Ethiopia

Gebrekidan Worku, Addis Ababa University
Ermias Teferi, Addis Ababa University
Amare Bantider, Addis Ababa University
Yihun T. Dile, Texas A&M AgriLife


This study evaluates bias correction methods and develops future climate scenarios using the output of a better bias correction technique at the Jemma sub-basin. The performance of different bias correction techniques was evaluated using several statistical metrics. The bias correction methods performance under climate condition different from the current climate was also evaluated using the differential split sample testing (DSST) and reveals that the distribution mapping technique is valid under climate condition different from the current climate. All bias correction methods were effective in adjusting mean monthly and annual RCM simulations of rainfall and temperature to the observed rainfall and temperature values. However, distribution mapping method was better in capturing the 90th percentile of observed rainfall and temperature and wet day probability of observed rainfall than other methods. As a result, we use the future (2021–2100) simulation of RCMs which are bias corrected using distribution mapping technique. The output of bias-adjusted RCMs unfolds a decline of rainfall, a persistent increase of temperature and an increase of extremes of rainfall and temperature in the future climate under emission scenarios of Representative Concentration Pathways 4.5, 8.5 and 2.6 (RCP4.5, RCP8.5 and RCP2.6). Thus, climate adaptation strategies that can provide optimal benefits under different climate scenarios should be developed to reduce the impact of future climate change.