Articles | Volume 20, issue 2
https://doi.org/10.5194/hess-20-685-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-20-685-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Technical note
| 
12 Feb 2016
Technical note |
| 12 Feb 2016
Technical Note: The impact of spatial scale in bias correction of climate model output for hydrologic impact studies
Santa Clara University, Civil Engineering Department, Santa Clara,
CA 95053-0563, USA
D. L. Ficklin
Indiana University, Department of Geography, Bloomington, IN 47405,
USA
W. Wang
California State University at Monterey Bay, Department of Science and
Environmental Policy and NASA Ames Research Center, Moffett Field, CA 94035,
USA
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Cited
13 citations as recorded by crossref.
- Hybrid downscaling of sea surface salinity reveals future transition from freshening to salinification in coastal waters M. Nikoo https://doi.org/10.1016/j.ecss.2026.109788
- Review of bias correction methods for climate model outputs in hydrology A. Menapace et al. https://doi.org/10.1016/j.jhydrol.2025.133213
- Assessment of vegetation sediment reduction potential based on future climate system models, Loess Plateau, China T. Huang et al. https://doi.org/10.1016/j.iswcr.2026.100720
- Enumerating the Effects of Climate Change on Water Resources Using GCM Scenarios at the Xin’anjiang Watershed, China M. Zaman et al. https://doi.org/10.3390/w10101296
- An ERA‐5 Derived CONUS‐Wide High‐Resolution Precipitation Dataset Based on a Refined Parametric Statistical Downscaling Framework S. Emmanouil et al. https://doi.org/10.1029/2020WR029548
- Downscaling fire weather extremes from historical and projected climate models P. Jain et al. https://doi.org/10.1007/s10584-020-02865-5
- A parametric approach for simultaneous bias correction and high‐resolution downscaling of climate model rainfall A. Mamalakis et al. https://doi.org/10.1002/2016WR019578
- Station‐based non‐linear regression downscaling approach: A new monthly precipitation downscaling technique Z. Shen et al. https://doi.org/10.1002/joc.7158
- Projecting high-level dengue increases during low-incidence months in a large endemic urban area A. Costa et al. https://doi.org/10.1093/trstmh/traf052
- Future variation and uncertainty source decomposition in deep learning bias-corrected CMIP6 global extreme precipitation historical simulation X. Xiang et al. https://doi.org/10.3389/feart.2025.1601615
- High-resolution machine-learning downscaled climate projections and extreme hazard assessment over monsoon-dominated Odisha, India N. Kar & S. Sahu https://doi.org/10.1080/19475705.2026.2710404
- Anthropogenic climate change has slowed global agricultural productivity growth A. Ortiz-Bobea et al. https://doi.org/10.1038/s41558-021-01000-1
- An effective post-processing of the North American multi-model ensemble (NMME) precipitation forecasts over the continental US S. Khajehei et al. https://doi.org/10.1007/s00382-017-3934-0
13 citations as recorded by crossref.
- Hybrid downscaling of sea surface salinity reveals future transition from freshening to salinification in coastal waters M. Nikoo https://doi.org/10.1016/j.ecss.2026.109788
- Review of bias correction methods for climate model outputs in hydrology A. Menapace et al. https://doi.org/10.1016/j.jhydrol.2025.133213
- Assessment of vegetation sediment reduction potential based on future climate system models, Loess Plateau, China T. Huang et al. https://doi.org/10.1016/j.iswcr.2026.100720
- Enumerating the Effects of Climate Change on Water Resources Using GCM Scenarios at the Xin’anjiang Watershed, China M. Zaman et al. https://doi.org/10.3390/w10101296
- An ERA‐5 Derived CONUS‐Wide High‐Resolution Precipitation Dataset Based on a Refined Parametric Statistical Downscaling Framework S. Emmanouil et al. https://doi.org/10.1029/2020WR029548
- Downscaling fire weather extremes from historical and projected climate models P. Jain et al. https://doi.org/10.1007/s10584-020-02865-5
- A parametric approach for simultaneous bias correction and high‐resolution downscaling of climate model rainfall A. Mamalakis et al. https://doi.org/10.1002/2016WR019578
- Station‐based non‐linear regression downscaling approach: A new monthly precipitation downscaling technique Z. Shen et al. https://doi.org/10.1002/joc.7158
- Projecting high-level dengue increases during low-incidence months in a large endemic urban area A. Costa et al. https://doi.org/10.1093/trstmh/traf052
- Future variation and uncertainty source decomposition in deep learning bias-corrected CMIP6 global extreme precipitation historical simulation X. Xiang et al. https://doi.org/10.3389/feart.2025.1601615
- High-resolution machine-learning downscaled climate projections and extreme hazard assessment over monsoon-dominated Odisha, India N. Kar & S. Sahu https://doi.org/10.1080/19475705.2026.2710404
- Anthropogenic climate change has slowed global agricultural productivity growth A. Ortiz-Bobea et al. https://doi.org/10.1038/s41558-021-01000-1
- An effective post-processing of the North American multi-model ensemble (NMME) precipitation forecasts over the continental US S. Khajehei et al. https://doi.org/10.1007/s00382-017-3934-0
Saved (final revised paper)
Latest update: 28 Sep 2026
Short summary
To translate climate model output from its native coarse scale to a finer scale more representative of that at which societal impacts are experienced, a common method applied is statistical downscaling. A component of many statistical downscaling techniques is quantile mapping (QM). QM can be applied at different spatial scales, and here we study how skill varies with spatial scale. We find the highest skill is generally obtained when applying QM at approximately a 50 km spatial scale.
To translate climate model output from its native coarse scale to a finer scale more...
