Piecewise‐quantile mapping improves bias correction of global climate model daily precipitation towards preserving quantiles and extremes

Abstract

Bias correction is a vital technique to correct the climate model outputs for regional studies. Prior bias correction methods such as quantile mapping (QM) may be problematic in correcting extreme values. Herein we proposed a novel bias correction method, that is, the piecewise‐quantile mapping (PQM), by combing piecewise mapping with QM, which implements bias corrections on both extreme and nonextreme data. We compared the PQM method and other six commonly used methods in correcting daily precipitation in the Mekong River Basin (MRB) and the Yangtze River Basin (YRB) from two global climate models (GCMs) (i.e., EC‐Earth3 and MPI‐ESM1‐2‐HR) in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Our results show that the PQM perform better in bias correction pertaining to both the precipitation percentiles and most of the extremes indices. For example, the mean absolute error of R20mm (the number of days when precipitation ≥20 mm) is significantly reduced from 21.64 to 9.03 days and from 10.96 to 5.71 days over MRB and YRB, respectively. In addition, the PQM could capture the spatial distributions of extremes indices reasonably well. The PQM developed in this study provides more accurate bias correction of the GCMs outputs, which will reduce the uncertainty in the subsequent analyses, such as climate change impacts on hydrological and biogeochemical cycles, particularly under extreme conditions.

High Sensitivity of Compound Drought and Heatwave Events to Global Warming in the Future

“…Basically, the simulated climate variables from GCMs always have some biases compared to the observed data (Chen et al., 2021). To minimize the biases and improve the accuracy of climate model outputs, we employ the widely‐used Quantile Mapping (QM) method (Cannon et al., 2015; Maraun, 2013; Q. Zhang, Gan, et al., 2022) to correct the monthly precipitation and daily Tmax from GCMs outputs, and correct the monthly potential evapotranspiration (PET) calculated by the Penman–Monteith (PM) equation using the 11 meteorological variables in Table S1 in Supporting Information S1. The QM bias correction method is one of the statistical downscaling methods, which attempts to find a transfer function to obtain the best fit in mapping the simulated cumulative distribution function of the variable onto the observed cumulative distribution function (Themeβl et al., 2012; Q. Zhang, Gan, et al., 2022).…”

“…Basically, the simulated climate variables from GCMs always have some biases compared to the observed data (Chen et al, 2021). To minimize the biases and improve the accuracy of climate model outputs, we employ the widely-used Quantile Mapping (QM) method (Cannon et al, 2015;Maraun, 2013;Q. Zhang, Gan, et al, 2022) to correct the monthly precipitation and daily Tmax from GCMs outputs, and correct the monthly potential evapotranspiration (PET) calculated by the Penman-Monteith (PM) equation using the 11 meteorological variables in Table S1 in Supporting Information S1.…”

“…Zhang, Gan, et al, 2022) to correct the monthly precipitation and daily Tmax from GCMs outputs, and correct the monthly potential evapotranspiration (PET) calculated by the Penman-Monteith (PM) equation using the 11 meteorological variables in Table S1 in Supporting Information S1. The QM bias correction method is one of the statistical downscaling methods, which attempts to find a transfer function to obtain the best fit in mapping the simulated cumulative distribution function of the variable onto the observed cumulative distribution function (Themeβl et al, 2012;Q. Zhang, Gan, et al, 2022).…”