Bias correction of daily precipitation simulated by a regional climate model: a comparison of methods

TLDR

Quantifying future changes in precipitation extremes is crucial for assessing hydrological vulnerability, yet climate models often misrepresent daily precipitation, and bias correction adds further uncertainty, especially for higher‑order moments. The study compares four bias‑correction techniques—linear, nonlinear, γ‑based quantile mapping, and empirical quantile mapping—applied to regional climate model precipitation. The authors evaluated each method by computing errors in the first four statistical moments of daily precipitation time series from the HadRM3.0‑PPE‑UK model and comparing them to gridded rain‑gauge data for seven Great Britain catchments, using cross‑validation across calibration periods. Results show that first and second moments can be robustly corrected, while third and fourth moments are highly sensitive to the bias‑correction method and calibration period; γ‑based quantile mapping performs best when both datasets fit a γ distribution, nonlinear mapping is preferable otherwise, linear mapping is least sensitive to calibration period, and empirical mapping is highly accurate but also most sensitive. © 2012 Royal Meteorological Society.

Abstract

Abstract Quantifying the effects of future changes in the frequency of precipitation extremes is a key challenge in assessing the vulnerability of hydrological systems to climate change but is difficult as climate models do not always accurately simulate daily precipitation. This article compares the performance of four published techniques used to reduce the bias in a regional climate model precipitation output: (1) linear, (2) nonlinear, (3) γ‐based quantile mapping and (4) empirical quantile mapping. Overall performance and sensitivity to the choice of calibration period were tested by calculating the errors in the first four statistical moments of generated daily precipitation time series and using a cross‐validation technique. The study compared the 1961–2005 precipitation time series from the regional climate model HadRM3.0‐PPE‐UK (unperturbed version) with gridded daily precipitation time series derived from rain gauges for seven catchments spread throughout Great Britain. We found that while the first and second moments of the precipitation frequency distribution can be corrected robustly, correction of the third and fourth moments of the distribution is much more sensitive to the choice of bias correction procedure and to the selection of a particular calibration period. Overall, our results demonstrate that, if both precipitation data sets can be approximated by a γ‐distribution, the γ‐based quantile‐mapping technique offers the best combination of accuracy and robustness. In circumstances where precipitation data sets cannot adequately be approximated using a γ‐distribution, the nonlinear method is more effective at reducing the bias, but the linear method is least sensitive to the choice of calibration period. The empirical quantile mapping method can be highly accurate, but results were very sensitive to the choice of calibration time period. However, it should be borne in mind that bias correction introduces additional uncertainties, which are greater for higher order moments. Copyright © 2012 Royal Meteorological Society