Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes?

Abstract

Quantile mapping bias correction algorithms are commonly used to correct systematic distributional biases in precipitation outputs from climate models. While effective at removing historical biases relative to observations, it has been found that quantile mapping can artificially corrupt future model-projected trends. Previous studies on the modification of precipitation trends by quantile mapping have focused on mean quantities, with less attention paid to extremes. Here we investigate the extent to which quantile mapping algorithms modify global climate model (GCM) trends in mean precipitation and precipitation extremes indices. First, we present a bias correction algorithm, quantile delta mapping (QDM), that explicitly preserves relative changes in precipitation quantiles. QDM is compared on synthetic data with detrended quantile mapping (DQM), which is designed to preserve trends in the mean, and standard quantile mapping (QM). Next, methods are applied to Coupled Model Intercomparison Project...