An intercomparison of climate model bias correction methods across Australia
5 affiliations
- 1ACSIRO Environment, Hobart, Tas., Australia
- 2BCSIRO Environment, Melbourne, Vic., Australia
- 3CBureau of Meteorology, Melbourne, Vic., Australia
- 4DSchool of Geography, Earth and Atmospheric Sciences, University of Melbourne, Melbourne, Vic., Australia
- 5ESchool of Mathematics and Statistics, University of New South Wales, Sydney, NSW, Australia
Abstract
The National Partnership for Climate Projections (NPCP) was established as a collaborative effort of the Australian climate projections community to develop a consistent approach to deliver future climate information. As bias correction of climate model outputs is important for many applications, a NPCP bias correction intercomparison project was initiated. The first phase of the intercomparison aimed to support the production of national-scale climate projections by the Australian Climate Service. It focused on five methods – Equidistant Cumulative Density Function matching (ECDFm), Quantile Matching for Extremes (QME), Quantile Delta Change (QDC), N-Dimensional Multi-Variate Bias Correction (MBCn) and Multivariate Recursive Nesting Bias Correction (MRNBC) – and applied them to daily timescale Coordinated Regional Climate Downscaling Experiment (CORDEX) data produced by NPCP partner organisations. Each method was assessed over a calibration period and also via cross-validation on several metrics relating to the temperature and precipitation climatology, variability, distribution, extremes and trends. The best-performing bias correction methods were QME and MRNBC. The ECDFm method also performed well on most metrics, but under certain circumstances it could dramatically increase the model bias. The QDC method is a delta change method (i.e. it perturbs the observations rather than correcting model data) and compared very favourably to the four bias correction methods. The QME, MRNBC and QDC methods were subsequently used by the Australian Climate Service to produce climate projections datasets for Australia.
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BibTeX
@article{irving2026intercomparison,
title = {{An intercomparison of climate model bias correction methods across Australia}},
author = {Damien Irving and Alicia Takbash and Justin Peter and Andrew Gammon and Andrew J. Dowdy and Thi Lan Dao and Arpit Kapoor and Mitchell Black and Dörte Jakob and Michael Grose},
journal = {Journal of Southern Hemisphere Earth System Science},
year = {2026},
month = may,
doi = {10.1071/es25023},
url = {https://doi.org/10.1071/es25023},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - An intercomparison of climate model bias correction methods across Australia
AU - Damien Irving
AU - Alicia Takbash
AU - Justin Peter
AU - Andrew Gammon
AU - Andrew J. Dowdy
AU - Thi Lan Dao
AU - Arpit Kapoor
AU - Mitchell Black
AU - Dörte Jakob
AU - Michael Grose
PY - 2026
DA - 2026/05/14
JO - Journal of Southern Hemisphere Earth System Science
DO - 10.1071/es25023
UR - https://doi.org/10.1071/es25023
AB - The National Partnership for Climate Projections (NPCP) was established as a collaborative effort of the Australian climate projections community to develop a consistent approach to deliver future climate information. As bias correction of climate model outputs is important for many applications, a NPCP bias correction intercomparison project was initiated. The first phase of the intercomparison aimed to support the production of national-scale climate projections by the Australian Climate Service. It focused on five methods – Equidistant Cumulative Density Function matching (ECDFm), Quantile Matching for Extremes (QME), Quantile Delta Change (QDC), N-Dimensional Multi-Variate Bias Correction (MBCn) and Multivariate Recursive Nesting Bias Correction (MRNBC) – and applied them to daily timescale Coordinated Regional Climate Downscaling Experiment (CORDEX) data produced by NPCP partner organisations. Each method was assessed over a calibration period and also via cross-validation on several metrics relating to the temperature and precipitation climatology, variability, distribution, extremes and trends. The best-performing bias correction methods were QME and MRNBC. The ECDFm method also performed well on most metrics, but under certain circumstances it could dramatically increase the model bias. The QDC method is a delta change method (i.e. it perturbs the observations rather than correcting model data) and compared very favourably to the four bias correction methods. The QME, MRNBC and QDC methods were subsequently used by the Australian Climate Service to produce climate projections datasets for Australia.
SN - 2206-5865
ER -