Articles | Volume 28, issue 9
https://doi.org/10.5194/hess-28-1981-2024
© 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/hess-28-1981-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assessing downscaling techniques for frequency analysis, total precipitation and rainy day estimation in CMIP6 simulations over hydrological years
David A. Jimenez
CORRESPONDING AUTHOR
Hydraulic and Water Resources Department, Federal University of Minas Gerais UFMG, 31270-901, MG, Belo Horizonte, Brazil
Faculty of Engineering, Free University of Bozen-Bolzano, 39100 Bolzano, Italy
Ariele Zanfei
AIAQUA S.r.l., Via Volta 13/A, Bolzano, Italy
Eber José de Andrade Pinto
Hydraulic and Water Resources Department, Federal University of Minas Gerais UFMG, 31270-901, MG, Belo Horizonte, Brazil
The Geological Survey of Brazil, 30140-002, MG, Belo Horizonte, Brazil
Bruno Brentan
Hydraulic and Water Resources Department, Federal University of Minas Gerais UFMG, 31270-901, MG, Belo Horizonte, Brazil
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Cited
15 citations as recorded by crossref.
- Changes in the frequencies of occurrence of maximum daily precipitation for the metropolitan region of Belo Horizonte – Brazil D. Jimenez et al. https://doi.org/10.1016/j.uclim.2025.102461
- Performance evaluation of CMIP6 global climate models using ERA5 over Indian Monsoon Region A. Sreekumar & P. Valayamkunnath https://doi.org/10.1007/s00382-025-07895-2
- New insights from the bias-corrected simulations of CMIP6 in Northern Hemisphere’s snow drought Y. Hu et al. https://doi.org/10.1038/s43247-026-03187-7
- Evaluating the future of climate modeling: A critical review of CMIP6 applications and their role in climate risk and impact assessments R. Guria et al. https://doi.org/10.1016/j.pce.2026.104591
- A downscaling method for CMIP6 precipitation based on the Bayesian ridge spatiotemporal weighted regression model J. Yu et al. https://doi.org/10.1007/s00382-026-08185-1
- Spatiotemporal Dynamics and Future Projections of Carbon Use Efficiency on the Mongolian Plateau: A Remote Sensing and Machine Learning Approach X. Yang et al. https://doi.org/10.3390/rs17081392
- An impact-driven framework for climate model evaluation M. Elling et al. https://doi.org/10.1007/s10584-026-04157-w
- A Climate-Informed Multi-Model Framework for Probabilistic Intensity–Duration–Frequency Curves Using CMIP6 Projections and Probabilistic Uncertainty Analysis: A Case Study of Makkah, Saudi Arabia B. Ullah et al. https://doi.org/10.3390/w18161965
- Assessing future precipitation patterns in the Brantas River Basin through downscaling of CMIP6 GCMs G. Herlambang & L. Jaelani https://doi.org/10.1088/1755-1315/1607/1/012036
- ECHIDNA: Extreme Climate Historical and Future Indices Data under Numerous Approaches across Major Chinese River Basins Based on CMIP6 Multi-Model Ensemble L. Xinlong et al. https://doi.org/10.1038/s41597-026-07510-x
- An examination of the complex climate risks and management approaches confronting the Northeast Tiger and Leopard National Park in the future Y. Wu et al. https://doi.org/10.1038/s41598-026-52830-y
- Hydro-climatological changes and mine water management challenges in Canada under climate change W. Palash et al. https://doi.org/10.1088/1748-9326/adf762
- Projecting precipitation extremes over Cyprus using CMIP6 climate scenarios N. Gödek Hayal et al. https://doi.org/10.1007/s11069-026-08119-4
- Occurrence Frequency Projection of Rainfall-Induced Landslides Under Climate Change in Chongqing, China J. Wang et al. https://doi.org/10.3390/w18020178
- Evaluating Climatic Trends through Mann-Kendall and Sen Slope Analysis of CMIP6 Model Downscaled Future Projections A. Balvanshi et al. https://doi.org/10.1007/s11269-026-04715-y
15 citations as recorded by crossref.
- Changes in the frequencies of occurrence of maximum daily precipitation for the metropolitan region of Belo Horizonte – Brazil D. Jimenez et al. https://doi.org/10.1016/j.uclim.2025.102461
- Performance evaluation of CMIP6 global climate models using ERA5 over Indian Monsoon Region A. Sreekumar & P. Valayamkunnath https://doi.org/10.1007/s00382-025-07895-2
- New insights from the bias-corrected simulations of CMIP6 in Northern Hemisphere’s snow drought Y. Hu et al. https://doi.org/10.1038/s43247-026-03187-7
- Evaluating the future of climate modeling: A critical review of CMIP6 applications and their role in climate risk and impact assessments R. Guria et al. https://doi.org/10.1016/j.pce.2026.104591
- A downscaling method for CMIP6 precipitation based on the Bayesian ridge spatiotemporal weighted regression model J. Yu et al. https://doi.org/10.1007/s00382-026-08185-1
- Spatiotemporal Dynamics and Future Projections of Carbon Use Efficiency on the Mongolian Plateau: A Remote Sensing and Machine Learning Approach X. Yang et al. https://doi.org/10.3390/rs17081392
- An impact-driven framework for climate model evaluation M. Elling et al. https://doi.org/10.1007/s10584-026-04157-w
- A Climate-Informed Multi-Model Framework for Probabilistic Intensity–Duration–Frequency Curves Using CMIP6 Projections and Probabilistic Uncertainty Analysis: A Case Study of Makkah, Saudi Arabia B. Ullah et al. https://doi.org/10.3390/w18161965
- Assessing future precipitation patterns in the Brantas River Basin through downscaling of CMIP6 GCMs G. Herlambang & L. Jaelani https://doi.org/10.1088/1755-1315/1607/1/012036
- ECHIDNA: Extreme Climate Historical and Future Indices Data under Numerous Approaches across Major Chinese River Basins Based on CMIP6 Multi-Model Ensemble L. Xinlong et al. https://doi.org/10.1038/s41597-026-07510-x
- An examination of the complex climate risks and management approaches confronting the Northeast Tiger and Leopard National Park in the future Y. Wu et al. https://doi.org/10.1038/s41598-026-52830-y
- Hydro-climatological changes and mine water management challenges in Canada under climate change W. Palash et al. https://doi.org/10.1088/1748-9326/adf762
- Projecting precipitation extremes over Cyprus using CMIP6 climate scenarios N. Gödek Hayal et al. https://doi.org/10.1007/s11069-026-08119-4
- Occurrence Frequency Projection of Rainfall-Induced Landslides Under Climate Change in Chongqing, China J. Wang et al. https://doi.org/10.3390/w18020178
- Evaluating Climatic Trends through Mann-Kendall and Sen Slope Analysis of CMIP6 Model Downscaled Future Projections A. Balvanshi et al. https://doi.org/10.1007/s11269-026-04715-y
Saved (final revised paper)
Latest update: 19 Aug 2026
Short summary
Most studies that aim to identify the impacts of climate change employ general circulation models. However, due to their low spatial resolution, it is necessary to apply downscaling techniques. This work assesses the performance of three methodologies in developing frequency analyses and estimating the number of rainy days and total precipitation per year. Quantile mapping and regression trees excelled in frequency analysis, and the delta method best estimated multiyear total precipitation.
Most studies that aim to identify the impacts of climate change employ general circulation...