Articles | Volume 19, issue 15
https://doi.org/10.5194/gmd-19-7457-2026
https://doi.org/10.5194/gmd-19-7457-2026
Model description paper
 | 
13 Aug 2026
Model description paper |  | 13 Aug 2026

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

Youngil Kim and Jason P. Evans

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Cited articles

Bruyere, C. L., Done, J. M., Holland, G. J., and Fredrick, S.: Bias corrections of global models for regional climate simulations of high-impact weather, Clim. Dynam., 43, 1847–1856, https://doi.org/10.1007/s00382-013-2011-6, 2014. 
Cannon, A. J.: Multivariate Bias Correction of Climate Model Output: Matching Marginal Distributions and Intervariable Dependence Structure, J. Climate, 29, 7045–7064, https://doi.org/10.1175/Jcli-D-15-0679.1, 2016. 
Cannon, A. J.: Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables, Clim. Dynam., 50, 31–49, https://doi.org/10.1007/s00382-017-3580-6, 2018. 
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2023. 
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Short summary
Climate models used to study future climate often contain systematic errors that affect high-resolution simulations. This study presents a new open-source tool that reduces these errors before regional climate simulations are run. By correcting multiple atmospheric variables together and at short time scales, the method improves realism and consistency in simulated climate patterns. This leads to more reliable regional projections, particularly for extreme weather events.
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