Selecting CMIP6 precipitation models by integrating relative importance metrics, compromise programming index, and
Farnaz Ershadfath1, Rouhollah Davarpanah2, Zulfaqar Sa'adi3
1Water Engineering Department, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, 65178-38695, Iran.
The Science of the Total Environment
|November 13, 2025
Summary
This study ranks climate models for Iran
Area of Science:
- Climate Science
- Hydrology
- Environmental Science
Background:
- Accurate climate projections are vital for water resource management.
- General Circulation Models (GCMs) require rigorous assessment for reliable future climate predictions.
- Advanced GCM ranking methods are needed for regional applications.
Purpose of the Study:
- To develop and apply a novel framework for ranking GCMs for precipitation projection in Iran.
- To identify the most suitable CMIP6 GCMs for assessing spatiotemporal precipitation changes.
- To project future precipitation patterns under different climate scenarios.
Main Methods:
- A novel framework combining Relative Importance Metrics (RIMs), Compromise Programming Index (CPI), and Jenks Optimized Classification (JOC) was developed.
- 14 CMIP6 GCMs were evaluated using ERA5 and CHIRPS data (1985-2014).
- The top-ranked MPI-ESM1-2-LR model was bias-corrected and used for future precipitation projections (2025-2084) under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios.
Main Results:
- MPI-ESM1-2-LR was identified as the top-ranked GCM for Iran.
- Near-future projections show mixed precipitation changes, while far-future projections indicate significant declines under SSP2-4.5 and SSP5-8.5.
- Over 90% of Iran faces water scarcity risks in the far future, with substantial precipitation reductions from February to September.
Conclusions:
- The novel ranking framework effectively identifies suitable GCMs for regional climate impact studies.
- Future climate projections highlight significant water scarcity risks for Iran, particularly in the long term.
- Targeted water management strategies are crucial to mitigate the projected impacts of reduced precipitation.
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