Articles | Volume 17, issue 22
https://doi.org/10.5194/gmd-17-8173-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-8173-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
19 Nov 2024
Development and technical paper |
| 19 Nov 2024
| 19 Nov 2024
Robust handling of extremes in quantile mapping – “Murder your darlings”
Swedish Meteorological and Hydrological Institute, Folkborgsvägen 17, 601 76 Norrköping, Sweden
Thomas Bosshard
Swedish Meteorological and Hydrological Institute, Folkborgsvägen 17, 601 76 Norrköping, Sweden
Denica Bozhinova
Swedish Meteorological and Hydrological Institute, Folkborgsvägen 17, 601 76 Norrköping, Sweden
Lars Bärring
Swedish Meteorological and Hydrological Institute, Folkborgsvägen 17, 601 76 Norrköping, Sweden
Joakim Löw
Swedish Meteorological and Hydrological Institute, Folkborgsvägen 17, 601 76 Norrköping, Sweden
Carolina Nilsson
Swedish Meteorological and Hydrological Institute, Folkborgsvägen 17, 601 76 Norrköping, Sweden
Gustav Strandberg
Swedish Meteorological and Hydrological Institute, Folkborgsvägen 17, 601 76 Norrköping, Sweden
Johan Södling
Swedish Meteorological and Hydrological Institute, Folkborgsvägen 17, 601 76 Norrköping, Sweden
Johan Thuresson
Swedish Meteorological and Hydrological Institute, Folkborgsvägen 17, 601 76 Norrköping, Sweden
Renate Wilcke
Swedish Meteorological and Hydrological Institute, Folkborgsvägen 17, 601 76 Norrköping, Sweden
Swedish Meteorological and Hydrological Institute, Folkborgsvägen 17, 601 76 Norrköping, Sweden
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Cited
11 citations as recorded by crossref.
- Hydrologically relevant bias correction of GPM-IMERG: Performance across quantiles in upland tropical watershed E. Suhartanto et al. https://doi.org/10.1016/j.rineng.2026.110760
- Seasonal Bias Correction of Daily Precipitation over France Using a Stitch Model Designed for Robust Representation of Extremes P. Ear et al. https://doi.org/10.3390/atmos16040480
- Improving satellite and reanalysis precipitation estimates in a Himalayan River Basin: a comparative study of bias correction methods with focus on extremes and ensemble method performance H. Tiwari & R. Garg https://doi.org/10.1007/s12665-025-12626-1
- Assessment of seasonal and extreme rainfall over Kerala using bias-corrected high-resolution climate models: historical and future changes M. Reji et al. https://doi.org/10.1007/s00382-026-08261-6
- Building retrofitting towards net zero energy under climate change: Application of a machine learning model M. IBRAHIM et al. https://doi.org/10.1016/j.energy.2026.141877
- A Bias-Corrected HighResMIP Dataset for Impact Assessment Studies F. Yakubu et al. https://doi.org/10.1038/s41597-026-07709-y
- Review of bias correction methods for climate model outputs in hydrology A. Menapace et al. https://doi.org/10.1016/j.jhydrol.2025.133213
- Enhancing GEOGLOWS River Forecast System with a High-Resolution Pre-Processing Approach for Runoff Bias Correction J. Chancay et al. https://doi.org/10.3390/hydrology13050128
- Impact of Climate Change on Reference Evapotranspiration: Bias Assessment and Climate Models in a Semi-Arid Agricultural Zone O. Galván-Cano et al. https://doi.org/10.3390/w17213040
- Evaluating statistical downscaling and bias-correction methods for climate extremes across major Chinese River basins L. Xinlong et al. https://doi.org/10.1016/j.asej.2026.104186
- SC-PREC4SA: A serially complete daily precipitation dataset for South America A. Huerta et al. https://doi.org/10.1038/s41597-025-05312-1
11 citations as recorded by crossref.
- Hydrologically relevant bias correction of GPM-IMERG: Performance across quantiles in upland tropical watershed E. Suhartanto et al. https://doi.org/10.1016/j.rineng.2026.110760
- Seasonal Bias Correction of Daily Precipitation over France Using a Stitch Model Designed for Robust Representation of Extremes P. Ear et al. https://doi.org/10.3390/atmos16040480
- Improving satellite and reanalysis precipitation estimates in a Himalayan River Basin: a comparative study of bias correction methods with focus on extremes and ensemble method performance H. Tiwari & R. Garg https://doi.org/10.1007/s12665-025-12626-1
- Assessment of seasonal and extreme rainfall over Kerala using bias-corrected high-resolution climate models: historical and future changes M. Reji et al. https://doi.org/10.1007/s00382-026-08261-6
- Building retrofitting towards net zero energy under climate change: Application of a machine learning model M. IBRAHIM et al. https://doi.org/10.1016/j.energy.2026.141877
- A Bias-Corrected HighResMIP Dataset for Impact Assessment Studies F. Yakubu et al. https://doi.org/10.1038/s41597-026-07709-y
- Review of bias correction methods for climate model outputs in hydrology A. Menapace et al. https://doi.org/10.1016/j.jhydrol.2025.133213
- Enhancing GEOGLOWS River Forecast System with a High-Resolution Pre-Processing Approach for Runoff Bias Correction J. Chancay et al. https://doi.org/10.3390/hydrology13050128
- Impact of Climate Change on Reference Evapotranspiration: Bias Assessment and Climate Models in a Semi-Arid Agricultural Zone O. Galván-Cano et al. https://doi.org/10.3390/w17213040
- Evaluating statistical downscaling and bias-correction methods for climate extremes across major Chinese River basins L. Xinlong et al. https://doi.org/10.1016/j.asej.2026.104186
- SC-PREC4SA: A serially complete daily precipitation dataset for South America A. Huerta et al. https://doi.org/10.1038/s41597-025-05312-1
Saved (final revised paper)
Latest update: 14 Jul 2026
Short summary
When bias adjusting climate model data using quantile mapping, one needs to prescribe what to do at the tails of the distribution, where a larger data range is likely encountered outside of the calibration period. The end result is highly dependent on the method used. We show that, to avoid discontinuities in the time series, one needs to exclude data in the calibration range to also activate the extrapolation functionality in that time period.
When bias adjusting climate model data using quantile mapping, one needs to prescribe what to do...
