| 04 Jan 2026
SDMBCv2 (v1.0): correcting systematic biases in RCM inputs for future projection
Abstract. Regional Climate Models (RCMs) offer enhanced spatial resolution and a more realistic depiction of local climate processes. However, they often inherit systematic biases from their driving Global Climate Models (GCMs), which can compromise the accuracy of downscaled climate projections. To address this, bias correction techniques have been widely employed to adjust GCM and RCM outputs, particularly for climate impact and adaptation studies. Traditional methods, however, typically correct surface variables independently and lack physical and dynamical consistency. Bias correcting GCM boundary conditions prior to RCM simulation ensures a more coherent, physically and dynamically consistent, regional climate simulation with reduced errors. This study evaluates the effectiveness of such an approach using a calibration/validation framework, demonstrating significant error reduction during the validation (out-of-sample) period compared to uncorrected GCM data. We present an updated version of the open-source Python package, Sub-Daily Multivariate Bias Correction (SDMBC) v2, designed to correct RCM input variables using both reanalysis and raw GCM datasets. Enhancements include support for future climate projections, flexible horizontal and vertical interpolation for compatibility with diverse datasets, and a fully Python-based architecture optimized for parallel processing and high-performance computing. This paper illustrates the software's capabilities and provides a practical application example.
The authors introduce the Python-based software package called SDMBCv2, which is designed for bias-correcting global climate models (GCMs) prior to input into regional climate models (RCMs) for dynamical downscaling. Using the ERA5 reanalysis dataset as their "observation" dataset, they were able to show general improvements to MAE and K-S scores after bias correcting the ACCESS-ESM1.5 GCM. The SDMBCv2 software can thus be useful for researchers conducting dynamical downscaling. In general, the article was well written and presented, and I recommend it for publication after Minor Revisions, as well as addressing some questions about the overall utility/performance of the software.
Scientific Questions:
1.) How long does it take to run SDMBCv2? I do wonder about its performance as a Python package. Though I understand the accessibility of a Python package for a wider audience, most researchers working with GCMs/RCMs would already have familiarity with Fortran. From a scalability standpoint, especially across ensembles of GCMs and for different CMIP6 pathways, wouldn't a Fortran package be more useful? Though I understand that xESMF and CDO form the core of SDMBCv2, I would appreciate if the authors could comment on performance improvements (or penalties) vs. a pure Fortran approach (e.g. SDMBCv1).
2.) Have the authors used SDMBCv2 for other GCMs other than ACCESS? Especially in regards to the reduction in pass rate for q in hour 18 (Table 1); if this issue would persist with other GCMs. More generally, whether or not the SDMBCv2 could be generalized to successfully bias correct other GCMs.
3.) Have the authors tried running an RCM using the bias-corrected ACCESS? From a historical climate downscaling perspective, what advantage would this have over say downscaling from a reanalysis dataset directly?
4.) Related to 3.), one of the big assumptions with bias correcting a climate dataset is stationarity of bias into the future. Given that the improvement in metrics in 1990-2020 are fairly moderate after bias correction, what assumptions can be made regarding the performance of this approach for future years (either for the GCM directly or after downscaling with an RCM)? Corollary to this, have the authors tried different calibration periods, and/or testing periods, to confirm any temporal aspects to their bias correction approach?
5.) Also related to 3.): there was an emphasis on being able to represent extreme events in the paper. Has this been verified, i.e. that this bias-correction approach could improve the representation of 95+-percentile events, particularly after downscaling with an RCM?
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Minor Comments:
- Recommend that any reference to "Observations" or "observed dataset" should be switched to "Reanalysis"Â Â
- e.g. Line 140: "raw" ---> "GCM"
- Why is SST evaluated on a seasonal timescale (Figure 2) while the variables are evaluated on a daily timescale (Figure 3)?
- In Figure 5, the bias-corrected plots show sizeable biases. How is the computed MAE 0.0?
- Table 1: SDMBC ---> should be SDMBCv2
- Line 440: "It's possible" ---> "It is possible"
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