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November 14, 2025The Science of The Total EnvironmentOpen Access

Selecting CMIP6 precipitation models by integrating relative importance metrics, compromise programming index, and Jenks optimized classification

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Authors

FEFarnaz ErshadfathRDRouhollah DavarpanahZSZulfaqar Sa'adi

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Overview

Nationwide study reveals precipitation modeling techniques may improve projections, highlighting risks of water scarcity in Iran.

Key Points

  • The aim is to enhance the reliability of General Circulation Model selection for precipitation projections in Iran.
  • Evaluated 14 CMIP6 GCMs using a framework integrating Relative Importance Metrics, Compromise Programming Index, and Jenks Optimized Classification.
  • Used gridded climate data from ERA5 and CHIRPS for the evaluation period of 1985-2014.
  • Top-ranked model MPI-ESM1-2-LR was bias-corrected using the Delta Change method.
  • Far future projections indicate water scarcity risks across over 90% of Iran's grid points, especially under SSP2-4.5 and SSP5-8.5 scenarios.
  • Precipitation increases forecasted from October to January, with substantial reductions from February to September, reaching up to -60% in the far future.

Cite This Study

Ershadfath et al. (2025) studied this question.

synapsesocial.com/papers/692519b4c0ce034ddc35453chttps://doi.org/10.1016/j.scitotenv.2025.180935
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