Narahari et al. (2026) CMIP6 model ranking and machine learning–assisted ensemble evaluation using a multi-variable integrated MCDM–GDM framework

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Identification

• Journal: Journal of Water and Climate Change
• Year: 2026
• Date: 2026-09-18
• Authors: Megavath Narahari, Nekram Rawal, Pramod Soni, Saurabh Maurya
• DOI: 10.2166/wcc.2026.131

Research Groups

• Indian Institute of Technology (IIT) Kharagpur
• National Centre for Medium Range Weather Forecasting (NCMRWF)
• Indian Meteorological Department (IMD)

Short Summary

This study proposes a Multi-Variable Integrated MCDM–GDM framework to evaluate and rank CMIP6 GCMs for precipitation, atmospheric maximum and minimum temperatures in the Upper Mahanadi River Basin, India. The proposed framework identifies FGOALS-g3, MPI-ESM1-2-LR, MIROC-ES2L, KIOST-ESM, and NESM3 as the top five CMIP6 models.

Objective

• Evaluate and rank CMIP6 GCMs for precipitation (Pr), atmospheric maximum and minimum temperatures (Tasmax and Tasmin) in the Upper Mahanadi River Basin, India.

Study Configuration

• Spatial Scale: Regional scale, focusing on the Upper Mahanadi River Basin, India.
• Temporal Scale: Long-term climate projections from CMIP6 GCMs.

Methodology and Data

• Models used: 23 (Pr), 25 (Tasmax), and 24 (Tasmin) bias-corrected CMIP6 models.
• Data sources: 0.25° gridded IMD observations.

Main Results

• The proposed framework identifies FGOALS-g3, MPI-ESM1-2-LR, MIROC-ES2L, KIOST-ESM, and NESM3 as the top five CMIP6 models.
• Mean ensembles built from these five models achieved strong correlation (R2 = 0.78, 0.77, and 0.84 for Pr, Tasmax, and Tasmin).
• Machine-learning ensembles yielded only incremental gains (ΔR2 = 0.005 to 0.013).

Contributions

• The proposed framework establishes a more reliable selection of CMIP6 models than single-variable approaches.
• It is replicable and can be effectively applied to flood prediction and hydrological applications in the study region and other monsoon-dominated river basins.

Funding

• This research was supported by the Ministry of Earth Sciences (MoES), Government of India, through a research grant.

Citation

@article{Narahari2026CMIP6,
  author = {Narahari, Megavath and Rawal, Nekram and Soni, Pramod and Maurya, Saurabh},
  title = {CMIP6 model ranking and machine learning–assisted ensemble evaluation using a multi-variable integrated MCDM–GDM framework},
  journal = {Journal of Water and Climate Change},
  year = {2026},
  doi = {10.2166/wcc.2026.131},
  url = {https://doi.org/10.2166/wcc.2026.131}
}