Intercomparison of bias correction methods for precipitation   of multiple GCMs across six continents

Song, Young Hoon; Chung, Eun-Sung

doi:https://doi.org/10.5194/gmd-18-8017-2025

Articles | Volume 18, issue 20

https://doi.org/10.5194/gmd-18-8017-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-18-8017-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 20

Methods for assessment of models

|

29 Oct 2025

Methods for assessment of models |

| 29 Oct 2025

Intercomparison of bias correction methods for precipitation of multiple GCMs across six continents

Young Hoon Song and Eun-Sung Chung

Supplement

https://doi.org/10.5194/gmd-18-8017-2025-supplement

Data sets

ERA5 hourly data on single levels from 1940 to present Copernicus Climate Change Service https://doi.org/10.24381/cds.adbb2d47

Historical Daily Precipitation Data of CMIP6 GCMs and ERA5 YoungHoon Song https://doi.org/10.6084/m9.figshare.27999167.v5

Model code and software

xclim: xarray-based climate data analytics (0.48.1) P. Bourgault et al. https://doi.org/10.5281/zenodo.10685050

Comprehensive Index Code (v1.0.0) Y. H. Song https://doi.org/10.5281/zenodo.14351816

Short summary

This study assessed three methods for correcting daily precipitation data: Quantile Delta Mapping, Empirical Quantile Mapping (EQM), and Detrended Quantile Mapping (DQM) using 11 GCMs. EQM performed best overall, offering reliable corrections and lower uncertainty. The best bias correction method for each grid is selected differently depending on the weighting case. The best bias correction method can vary depending on factors such as climate and terrain.