Quantile mapping bias correction algorithms are commonly used to correct systematic distributional biases in precipitation outputs from climate models. Although they are 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. This article investigates the extent to which quantile mapping algorithms modify global climate model (GCM) trends in mean precipitation and precipitation extremes indices. First, a bias correction algorithm, quantile delta mapping (QDM), that explicitly preserves relative changes in precipitation quantiles is presented. QDM is compared on synthetic data with detrended quantile mapping (DQM), which is designed to preserve trends in the mean, and with standard quantile mapping (QM). Next, methods are applied to phase 5 of the Coupled Model Intercomparison Project (CMIP5) daily precipitation projections over Canada. Performance is assessed based on precipitation extremes indices and results from a generalized extreme value analysis applied to annual precipitation maxima. QM can inflate the magnitude of relative trends in precipitation extremes with respect to the raw GCM, often substantially, as compared to DQM and especially QDM. The degree of corruption in the GCM trends by QM is particularly large for changes in long period return values. By the 2080s, relative changes in excess of +500% with respect to historical conditions are noted at some locations for 20-yr return values, with maximum changes by DQM and QDM nearing +240% and +140%, respectively, whereas raw GCM changes are never projected to exceed +120%.

Index (FWI) System, a complicated set of multivariate indices that characterizes the risk of wildfire, are then calculated and verified against observed values. Third, MBCn is used to correct biases in the spatial dependence structure of CanRCM4 precipitation fields. Results are compared against a univariate quantile mapping algorithm, which neglects the dependence between variables, and two multivariate bias correction algorithms, each of which corrects a different form of inter-variable correlation structure. MBCn outperforms these alternatives, often by a large margin, particularly for annual maxima of the FWI distribution and spatiotemporal autocorrelation of precipitation fields.

[1] This study investigated the sensitivity of streamflow to changes in climate and glacier cover for the Bridge River basin, British Columbia, using a semi-distributed conceptual hydrological model coupled with a glacier response model. Mass balance data were used to constrain model parameters. Climate scenarios included a continuation of the current climate and two transient GCM scenarios with greenhouse gas forcing. Modelled glacier mass balance was used to re-scale the glacier every decade using a volume-area scaling relation. Glacier area and summer streamflow declined strongly even under the steadyclimate scenario, with the glacier retreating to a new equilibrium within 100 years. For the warming scenarios, glacier retreat continued with no evidence of reaching a new equilibrium. Uncertainty in parameters governing glacier melt produced uncertainty in future glacier retreat and streamflow response. Where mass balance information is not available to assist with calibration, model-generated future scenarios will be subject to significant uncertainty.

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