Roulo et al. (2026) From Global to Basin‐Scale: Identifying Best‐Performing CMIP6 Models Across Indian River Basins Through Downscaled Precipitation Products

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: International Journal of Climatology
• Year: 2026
• Date: 2026-04-05
• Authors: Dinesh Roulo, Subbarao Pichuka, Ram Parameswar Menon
• DOI: 10.1002/joc.70367

Research Groups

[Information not available in the abstract.]

Short Summary

This study introduces a novel four-level framework to evaluate and rank 24 CMIP6 Global Climate Models (GCMs) across 27 Indian River Basins using a Multi-Metric Evaluation Approach. It finds significant spatial variability in model performance and highlights that GCMs performing well for mean rainfall do not necessarily capture extreme rainfall behavior accurately.

Objective

• To introduce a novel four-level framework for evaluating and ranking 24 CMIP6 Global Climate Models (GCMs) from the NEX‐GDDP‐CMIP6 dataset across 27 Indian River Basins.
• To minimize biases between climate projections and observations for basin-scale climate impact assessments (BCIA).

Study Configuration

• Spatial Scale: 27 Indian River Basins (IRBs) in the Indian Mainland (IM). Data provided at 0.25° × 0.25° spatial resolution.
• Temporal Scale: 1981–2010 (historical period).

Methodology and Data

• Models used: 24 GCMs from the NEX‐GDDP‐CMIP6 dataset (e.g., BCC‐CSM2‐MR, IPSL‐CM6A‐LR, MPI‐ESM1‐2‐LR, CMCC‐CM2‐SR5, MIROC6, CMCC-ESM2, INM-CM4-8, ACCESS-ESM1-5, GFDL-ESM4, MRI-ESM2-0, MPI-ESM1-2-HR, CanESM5).
• Data sources: Downscaled precipitation products from NEX‐GDDP‐CMIP6 (0.25° × 0.25° spatial resolution) evaluated against India Meteorological Department (IMD) gridded data.
• Methodology:
  • Level-1: Grid-wise assessment using six categorical and nine statistical performance metrics (e.g., correlation coefficient (CC)).
  • Level-2: Basin-wise performance evaluation using individual and cluster-wise statistical metrics.
  • Level-3: GCM evaluation using five Multi-Criteria Decision-Making (MCDM) methods: CP, CGT, TOPSIS, WA, and PROMETHEE-2.
  • Level-4: Group Decision-Making (GDM) approach (net strength method) to finalize consensus rankings per IRB.
  • Extreme rainfall evaluation using the 90th and 95th percentile rainfall across each grid.

Main Results

• A novel four-level framework was successfully introduced and applied to evaluate and rank 24 GCMs across 27 Indian River Basins.
• GCM rankings varied significantly depending on the metric/cluster used and showed considerable spatial variability across basins, with all 24 GCMs ranking in the top 5 for at least one basin.
• IPSL‐CM6A‐LR, CMCC‐ESM2, INM‐CM4‐8, ACCESS‐ESM1‐5, and GFDL‐ESM4 were identified as consistently skillful in representing extreme rainfall magnitude.
• MRI‐ESM2‐0, INM‐CM4‐8, MPI‐ESM1‐2‐HR, CanESM5, and IPSL‐CM6A‐LR performed well in capturing extreme rainfall frequency.
• A key finding is that GCMs performing well for mean rainfall do not necessarily accurately capture extreme rainfall behavior.

Contributions

• Introduction of a robust, novel four-level framework for comprehensive evaluation and ranking of CMIP6 GCMs for basin-scale climate impact assessments.
• Application of a Multi-Metric Evaluation Approach (MMEA) and Multi-Criteria Decision-Making (MCDM) methods to provide refined and consensus-based GCM rankings.
• Identification of specific GCMs that are best suited for representing mean versus extreme rainfall characteristics across Indian River Basins, highlighting the spatial variability of model performance.
• Provides a practical tool to facilitate the selection of optimal GCMs, thereby supporting resilient water resource planning and adaptive management strategies.

Funding

[Information not available in the abstract.]

Citation

@article{Roulo2026From,
  author = {Roulo, Dinesh and Pichuka, Subbarao and Menon, Ram Parameswar},
  title = {From Global to Basin‐Scale: Identifying Best‐Performing <scp>CMIP6</scp> Models Across Indian River Basins Through Downscaled Precipitation Products},
  journal = {International Journal of Climatology},
  year = {2026},
  doi = {10.1002/joc.70367},
  url = {https://doi.org/10.1002/joc.70367}
}