Adnan et al. (2026) Future outlook of monthly maximum daily precipitation in Pakistan’s hydroclimatic zones: high-resolution insights from CMIP6 multimodel data

Identification

• Journal: Scientific Reports
• Year: 2026
• Date: 2026-04-04
• Authors: Muhammad Adnan, Firdos Khan, Muhammad Abbas, Fahad Shahzad, TianXiang Yue
• DOI: 10.1038/s41598-026-45047-6

Research Groups

• State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
• College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
• School of Natural Sciences, National University of Sciences and Technology, Islamabad, Pakistan
• Scuola Universitaria Superiore Studi Pavia IUSS, Pavia, Italy
• University of Bergamo, Bergamo, Italy
• Precision Forestry Key Laboratory of Beijing, Beijing Forestry University, Beijing, China

Short Summary

This study projects future monthly maximum daily precipitation extremes (Rx1day) across Pakistan's seven hydroclimatic zones using bias-corrected CMIP6 multi-model ensembles under SSP2-4.5 and SSP5-8.5 scenarios, revealing significant spatial heterogeneity with northern highlands experiencing nearly double baseline values by late century.

Objective

• To project future monthly maximum daily precipitation extremes (Rx1day) in Pakistan's seven hydroclimatic zones using bias-corrected CMIP6 multi-model ensembles under SSP2-4.5 and SSP5-8.5 scenarios, addressing a gap in previous studies that largely emphasized seasonal or mean rainfall.

Study Configuration

• Spatial Scale: Pakistan, specifically its seven hydroclimatic zones.
• Temporal Scale:
  • Baseline: 1985–2014
  • Near future: 2017–2044
  • Mid-century: 2045–2072
  • Late century: 2073–2100

Methodology and Data

• Models used: CMIP6 multi-model ensembles (bias-corrected)
• Data sources: CMIP6 model data, observed data (available on request)

Main Results

• Projections show strong spatial heterogeneity in future monthly maximum daily precipitation extremes (Rx1day) across Pakistan.
• Northern and northwestern highlands are projected to experience the largest absolute increases, with late-century monsoon monthly maxima of daily precipitation reaching approximately 130–150 mm, nearly double baseline values.
• Central and southern zones also show pronounced amplification, intensifying flash-flood, riverine, and urban drainage hazards.
• Western arid and coastal regions are projected to experience a decline in the magnitude of monthly maximum daily precipitation, though punctuated by occasional high-intensity events.
• Intensification of extreme precipitation is most significant under the SSP5-8.5 scenario, where both the magnitude and spatial footprint of extremes expand over time.
• These localized shifts in extreme rainfall are highlighted as factors that can compound hazard exposure, destabilize agriculture, and overwhelm water-management systems.

Contributions

• Fills a critical gap in existing literature by providing high-resolution, zone-specific projections of monthly maximum daily precipitation extremes (Rx1day) for Pakistan, which are directly linked to flash floods, landslides, and infrastructure failures.
• Offers actionable evidence to strengthen early-warning capacity, guide resilient infrastructure planning, and inform targeted adaptation strategies in one of the world's most flood-exposed countries.

Funding

• National Natural Science Foundation of China (42330707)
• National Key Research & Development Program of China (2024YFD1700904)
• Science Fund for Creative Research Groups of the National Natural Science Foundation of China (72221002)
• Strategic Priority Research Program of Chinese Academy of Sciences (XDB0740100)
• National Key R&D Program of China (2021YFB3901300)

Citation

@article{Adnan2026Future,
  author = {Adnan, Muhammad and Khan, Firdos and Abbas, Muhammad and Shahzad, Fahad and Yue, TianXiang},
  title = {Future outlook of monthly maximum daily precipitation in Pakistan’s hydroclimatic zones: high-resolution insights from CMIP6 multimodel data},
  journal = {Scientific Reports},
  year = {2026},
  doi = {10.1038/s41598-026-45047-6},
  url = {https://doi.org/10.1038/s41598-026-45047-6}
}