Majhi et al. (2026) Do CMIP6 models reduce the uncertainty in sub-daily extreme precipitation projections?

Identification

• Journal: Journal of Hydrology
• Year: 2026
• Date: 2026-06-24
• Authors: Archana Majhi, Sonali Pattanayak, Chandrika Thulaseedharan Dhanya, Sumedha Chakma
• DOI: 10.1016/j.jhydrol.2026.135917

Research Groups

Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India; Willis Towers Watson India, I Think Techno Campus, Thane, Maharashtra 400606, India.

Short Summary

This study evaluates the uncertainty in sub-daily extreme precipitation projections using CMIP6 models and compares them with CMIP5 models. The results show that CMIP6 ensemble has lower uncertainty than CMIP5, but some indices exhibit increased uncertainty in specific regions.

Objective

• Investigate whether CMIP6 models reduce the uncertainty in sub-daily extreme precipitation projections compared to CMIP5 models.

Study Configuration

• Spatial Scale: Global land regions.
• Temporal Scale: Historical and future (projected) periods.

Methodology and Data

• Models used: CMIP6 GCMs (Coupled Model Intercomparison Project phase 6 General Circulation Models).
• Data sources: CMIP5 and CMIP6 datasets, satellite observations, and reanalysis data.

Main Results

• CMIP6 ensemble has lower uncertainty than CMIP5 in quartile ranges.
• CMIP6 uncertainty risk map shows a substantial increase in low uncertainty areas and a decline in hotspot areas for Simple Hourly-Precipitation Intensity Index, Wet Spell Length, and Annual Total Precipitation indices.
• However, Monthly MAXimum 3Hourly-precipitation (MAX3H) exhibits increased uncertainty particularly in West and East Africa, Southeast Asia, Amazon, and Northern Australian regions.

Contributions

• This study adds credibility to future sub-daily precipitation projections using CMIP6 models.
• It highlights the need for careful model evaluation against extreme precipitation, especially for tropics, and improvement in representation of vegetation and associated biogeochemical processes.

Funding

• Not specified.

Citation

@article{Majhi2026Do,
  author = {Majhi, Archana and Pattanayak, Sonali and Dhanya, Chandrika Thulaseedharan and Chakma, Sumedha},
  title = {Do CMIP6 models reduce the uncertainty in sub-daily extreme precipitation projections?},
  journal = {Journal of Hydrology},
  year = {2026},
  doi = {10.1016/j.jhydrol.2026.135917},
  url = {https://doi.org/10.1016/j.jhydrol.2026.135917}
}