Kim et al. (2026) SDMBCv2 (v1.0): correcting systematic biases in RCM inputs for future projection

Identification

• Journal: Geoscientific model development
• Year: 2026
• Date: 2026-08-13
• Authors: Youngil Kim, Jason P. Evans
• DOI: 10.5194/gmd-19-7457-2026

Research Groups

• Climate Change Research Centre, University of New South Wales, Sydney, Australia
• ARC Centre of Excellence for the Weather of the 21st Century, University of New South Wales, Sydney, Australia
• NSW Department of Climate Change, Energy, the Environment and Water, Sydney, NSW, Australia

Short Summary

This study evaluates the effectiveness of pre-downscaling bias correction in Regional Climate Models (RCMs) using a calibration/validation framework. The results show significant error reduction during the validation period compared to uncorrected Global Climate Model (GCM) data.

Objective

• To correct systematic biases in RCM inputs for future projection by adjusting GCM boundary conditions prior to RCM simulation.
• To evaluate the performance of bias correction using a calibration/validation framework.

Study Configuration

• Spatial Scale: Regional climate models with a spatial resolution of approximately 1.875° longitude × 1.25° latitude.
• Temporal Scale: Daily, monthly, and seasonal time scales.

Methodology and Data

• Models used: ACCESS-ESM1.5 (Australian Community Climate and Earth System Simulator Earth System Model Version 1.5) as the source GCM, and ERA5 (fifth-generation reanalysis dataset from the European Centre for Medium-Range Weather Forecasts) as the reference.
• Data sources: Reanalysis datasets (ERA5), raw GCM data (ACCESS-ESM1.5), and bias-corrected GCM outputs.

Main Results

• The study shows that pre-downscaling bias correction significantly reduces errors in key climatological statistics, including mean, standard deviation, lag-1 autocorrelation, and multivariate dependence.
• The bias-corrected GCM datasets show near-zero deviations across most regions for all variables, indicating good agreement with the reference.

Contributions

• This study provides a practical application example of pre-downscaling bias correction using the SDMBCv2 package.
• The results demonstrate the effectiveness of pre-downscaling bias correction in reducing errors and improving the accuracy of RCM outputs.

Funding

• ARC Centre of Excellence for the Weather of the 21st Century (University of New South Wales)
• Climate Change Research Centre, University of New South Wales

Citation

@article{Kim2026SDMBCv2,
  author = {Kim, Youngil and Evans, Jason P.},
  title = {SDMBCv2 (v1.0): correcting systematic biases in RCM inputs for future projection},
  journal = {Geoscientific model development},
  year = {2026},
  doi = {10.5194/gmd-19-7457-2026},
  url = {https://doi.org/10.5194/gmd-19-7457-2026}
}