Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods

Despite the increasing use of regional climate model (RCM) simulations in hydrological climate-change impact studies, their application is challenging due to the risk of considerable biases. To deal with these biases, several bias correction methods have been developed recently, ranging from simple scaling to rather sophisticated approaches. This paper provides a review of available bias correction methods and demonstrates how they can be used to correct for deviations in an ensemble of 11 different RCM-simulated temperature and precipitation series. The performance of all methods was assessed in several ways: At first, differently corrected RCM data was compared to observed climate data. The second evaluation was based on the combined influence of corrected RCM-simulated temperature and precipitation on hydrological simulations of monthly mean streamflow as well as spring and autumn flood peaks for five catchments in Sweden under current (1961–1990) climate conditions. Finally, the impact on hydrological simulations based on projected future (2021–2050) climate conditions was compared for the different bias correction methods. Improvement of uncorrected RCM climate variables was achieved with all bias correction approaches. While all methods were able to correct the mean values, there were clear differences in their ability to correct other statistical properties such as standard deviation or percentiles. Simulated streamflow characteristics were sensitive to the quality of driving input data: Simulations driven with bias-corrected RCM variables fitted observed values better than simulations forced with uncorrected RCM climate variables and had more narrow variability bounds. ⺠Several methods for correcting biased climate model output were analyzed. ⺠All reviewed correction methods are somewhat able to improve original climate model data. ⺠More sophisticated higher-skill approaches outperform simpler approaches. ⺠The choice of bias correction method is relevant for assessing hydrological change. ⺠The application of bias correction algorithms can considerably improve hydrological simulations.