Articles | Volume 16, issue 7
https://doi.org/10.5194/gmd-16-2055-2023
© Author(s) 2023. 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-16-2055-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
17 Apr 2023
Methods for assessment of models |
| 17 Apr 2023
| 17 Apr 2023
Evaluation of bias correction methods for a multivariate drought index: case study of the Upper Jhelum Basin
Department of Civil, Environmental, Architectural Engineering
and Mathematics, University of Brescia, Brescia, Italy
Department of Irrigation and Drainage, University of Agriculture
Faislabad, Faislabad, Pakistan
Ana Casanueva
Dept. Matemática Aplicada y Ciencias de la Computación (MACC), Universidad de Cantabria, Santander, Spain
Grupo de Meteorología y Computación, Unidad Asociada al CSIC por el IFCA, Santander, Spain
Muhammad Usman Liaqat
Department of Civil, Environmental, Architectural Engineering
and Mathematics, University of Brescia, Brescia, Italy
Giovanna Grossi
Department of Civil, Environmental, Architectural Engineering
and Mathematics, University of Brescia, Brescia, Italy
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Cited
20 citations as recorded by crossref.
- Assessing the impact of climate change on solar energy production in Italy R. Bonanno & E. Collino https://doi.org/10.1007/s10113-025-02417-6
- Drought characteristics in the Middle East simulated by raw and bias-corrected CMIP6 models B. Ghazi et al. https://doi.org/10.1016/j.scitotenv.2025.180468
- Intercomparison of bias correction methods for precipitation of multiple GCMs across six continents Y. Song & E. Chung https://doi.org/10.5194/gmd-18-8017-2025
- Added value of downscaling multivariate drought indices for seasonal climatic water-balance assessments E. Duzenli et al. https://doi.org/10.1088/1748-9326/ae5c22
- Multivariate bias correction of ERA5 using in-situ observations for planning and engineering D. Rasmussen https://doi.org/10.1088/2752-5295/ae63ee
- How likely is Ukraine to experience an extreme drought in the near future? A. Ballarin et al. https://doi.org/10.1007/s10584-025-03958-9
- Changes in the probability of temporally compound wet and dry events in a warmer world: case study in the Upper Jhelum Basin—South Asia R. Ansari et al. https://doi.org/10.1007/s10113-025-02367-z
- Terrestrial water storage in Australia under stress from compound climate extremes C. Ndehedehe et al. https://doi.org/10.1016/j.resenv.2025.100242
- Improving flood and drought management in transboundary Upper Jhelum Basin-South Asia R. Ansari et al. https://doi.org/10.1016/j.scitotenv.2024.174044
- The first emergence of unprecedented global water scarcity in the Anthropocene V. Ravinandrasana & C. Franzke https://doi.org/10.1038/s41467-025-63784-6
- Persistent river heatwaves are emerging worldwide under climate change Y. Chen et al. https://doi.org/10.1038/s41467-025-66868-5
- Drought intensification in Brazilian catchments: implications for water and land management A. Ballarin et al. https://doi.org/10.1088/1748-9326/ad3e18
- Multivariate bias correction and downscaling of climate models with trend-preserving deep learning F. Wang & D. Tian https://doi.org/10.1007/s00382-024-07406-9
- Applying a time-varying GEV distribution to correct bias in rainfall quantiles derived from regional climate models M. Onderka et al. https://doi.org/10.2478/johh-2024-0025
- Predicting aflatoxin risk with seasonal meteorological forecast D. Balková et al. https://doi.org/10.1088/1748-9326/ae3629
- How does bias correction impact simulated drought characteristics by Regional Climate Models? P. Nguyen-Ngoc-Bich et al. https://doi.org/10.1007/s10584-025-03901-y
- Assessing multivariate bias corrections of climate simulations on various impact models under climate change D. Allard et al. https://doi.org/10.5194/hess-29-4711-2025
- Integrating Climate Modelling, Downscaling, and Bias Correction for Drought Studies: Current Approaches and Future Directions C. Naresh & A. Mathew https://doi.org/10.1007/s11269-026-04843-5
- Future Streamflow and Hydrological Drought Under CMIP6 Climate Projections T. Liu et al. https://doi.org/10.3390/atmos16060691
- Comprehensive drought assessment based on the SPEI and its impact on crop production and streamflow in Afghanistan A. Samim et al. https://doi.org/10.1016/j.ejrh.2025.102893
20 citations as recorded by crossref.
- Assessing the impact of climate change on solar energy production in Italy R. Bonanno & E. Collino https://doi.org/10.1007/s10113-025-02417-6
- Drought characteristics in the Middle East simulated by raw and bias-corrected CMIP6 models B. Ghazi et al. https://doi.org/10.1016/j.scitotenv.2025.180468
- Intercomparison of bias correction methods for precipitation of multiple GCMs across six continents Y. Song & E. Chung https://doi.org/10.5194/gmd-18-8017-2025
- Added value of downscaling multivariate drought indices for seasonal climatic water-balance assessments E. Duzenli et al. https://doi.org/10.1088/1748-9326/ae5c22
- Multivariate bias correction of ERA5 using in-situ observations for planning and engineering D. Rasmussen https://doi.org/10.1088/2752-5295/ae63ee
- How likely is Ukraine to experience an extreme drought in the near future? A. Ballarin et al. https://doi.org/10.1007/s10584-025-03958-9
- Changes in the probability of temporally compound wet and dry events in a warmer world: case study in the Upper Jhelum Basin—South Asia R. Ansari et al. https://doi.org/10.1007/s10113-025-02367-z
- Terrestrial water storage in Australia under stress from compound climate extremes C. Ndehedehe et al. https://doi.org/10.1016/j.resenv.2025.100242
- Improving flood and drought management in transboundary Upper Jhelum Basin-South Asia R. Ansari et al. https://doi.org/10.1016/j.scitotenv.2024.174044
- The first emergence of unprecedented global water scarcity in the Anthropocene V. Ravinandrasana & C. Franzke https://doi.org/10.1038/s41467-025-63784-6
- Persistent river heatwaves are emerging worldwide under climate change Y. Chen et al. https://doi.org/10.1038/s41467-025-66868-5
- Drought intensification in Brazilian catchments: implications for water and land management A. Ballarin et al. https://doi.org/10.1088/1748-9326/ad3e18
- Multivariate bias correction and downscaling of climate models with trend-preserving deep learning F. Wang & D. Tian https://doi.org/10.1007/s00382-024-07406-9
- Applying a time-varying GEV distribution to correct bias in rainfall quantiles derived from regional climate models M. Onderka et al. https://doi.org/10.2478/johh-2024-0025
- Predicting aflatoxin risk with seasonal meteorological forecast D. Balková et al. https://doi.org/10.1088/1748-9326/ae3629
- How does bias correction impact simulated drought characteristics by Regional Climate Models? P. Nguyen-Ngoc-Bich et al. https://doi.org/10.1007/s10584-025-03901-y
- Assessing multivariate bias corrections of climate simulations on various impact models under climate change D. Allard et al. https://doi.org/10.5194/hess-29-4711-2025
- Integrating Climate Modelling, Downscaling, and Bias Correction for Drought Studies: Current Approaches and Future Directions C. Naresh & A. Mathew https://doi.org/10.1007/s11269-026-04843-5
- Future Streamflow and Hydrological Drought Under CMIP6 Climate Projections T. Liu et al. https://doi.org/10.3390/atmos16060691
- Comprehensive drought assessment based on the SPEI and its impact on crop production and streamflow in Afghanistan A. Samim et al. https://doi.org/10.1016/j.ejrh.2025.102893
Saved (final revised paper)
Latest update: 16 Aug 2026
Short summary
Bias correction (BC) has become indispensable to climate model output as a post-processing step to render output more useful for impact assessment studies. The current work presents a comparison of different state-of-the-art BC methods (univariate and multivariate) and BC approaches (direct and component-wise) for climate model simulations from three initiatives (CMIP6, CORDEX, and CORDEX-CORE) for a multivariate drought index (i.e., standardized precipitation evapotranspiration index).
Bias correction (BC) has become indispensable to climate model output as a post-processing step...
