Robust bias-correction of precipitation extremes using a novel hybrid empirical quantile-mapping method

High-resolution, daily precipitation climate products that realistically represent extremes are critical for evaluating local-scale climate impacts. A popular bias-correction method, empirical quantile mapping (EQM), can generally correct distributional discrepancies between simulated climate variables and observed data but can be highly sensitive to the choice of calibration period and is prone to overfitting. In this study, we propose a hybrid bias-correction method for precipitation, EQM-LIN, which combines the efficacy of EQM for correcting lower quantiles, with a robust linear correction for upper quantiles. We apply both EQM and EQM-LIN to historical daily precipitation data simulated by a regional climate model over a region in the northeastern USA. We validate our results using a five-fold cross-validation and quantify performance of EQM and EQM-LIN using skill score metrics and several climatological indices. As part of a high-resolution downscaling and bias-correction workflow, EQM-LIN significantly outperforms EQM in reducing mean, and especially extreme, daily distributional biases present in raw model output. EQM-LIN performed as good or better than EQM in terms of bias-correcting standard climatological indices (e.g., total annual rainfall, frequency of wet days, total annual extreme rainfall). In addition, our study shows that EQM-LIN is particularly resistant to overfitting at extreme tails and is much less sensitive to calibration data, both of which can reduce the uncertainty of bias-correction at extremes.