Evaluation of bias correction techniques for generating high-resolution daily temperature projections from CMIP6 models

Abstract

Due to the considerable biases in general circulation models (GCMs) simulation, bias correction methods are required and widely applied to reduce the model biases for impact studies. This study evaluated the performance of two bias correction methods, quantile delta mapping (QDM) and scaled distribution mapping (SDM), for generating high-resolution daily maximum temperature (Tmax) and minimum temperature (Tmin) projections for Canada using the latest GCMs from the Coupled Model Intercomparison Project phase 6 (CMIP6). CMIP6 GCMs show overall consistency with observations before and after bias correction, with better performance on Tmax compared to Tmin. QDM shows better performance relative to observations while SDM shows superior skill in preserving the raw climate signals. QDM and SDM methods are effective in reducing the biases of Tmax and Tmin for all GCMs. Both methods show similar skills in reproducing monthly probability distribution and capturing seasonal spatial patterns over Canada. Multi-model ensemble means have good performance in simulating the monthly mean of Tmax and Tmin but poor performance for high and low quantiles as well as standard deviation. QDM and SDM corrected ensemble means have the best performance. This study presents a comprehensive assessment of bias correction methods applications for individual CMIP6 GCMs and their multi-model ensemble means for high-resolution daily temperature predictions for Canada, providing a reference significance for bias correction studies as well as technical support for further impact assessment and adaptation planning around the world.