Yang et al. (2026) Systematic Overestimation of Global Peak Runoff Synchronization in CMIP6 Models

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Identification

• Journal: Geophysical Research Letters
• Year: 2026
• Date: 2026-09-25
• Authors: Yixin Yang, Gabriele Villarini, Long Yang
• DOI: 10.1029/2026gl122766

Research Groups

This study analyzes global synchronization of annual peak runoff using gridded datasets and a complex network approach, evaluating 13 CMIP6 models' ability to reproduce these patterns. It finds that CMIP6 models systematically overestimate connectivity and short-range synchronization due to exaggerated temporal dependence and shifted peak timing, potentially leading to an overestimation of compound flood risk.

Objective

• To analyze the global synchronization of annual peak runoff (a proxy for flooding) using gridded datasets and a complex network approach, and to evaluate 13 CMIP6 models in reproducing spatial synchronization patterns.

Study Configuration

• Spatial Scale: Global, gridded.
• Temporal Scale: Annual (focusing on peak runoff events).

Methodology and Data

• Models used: 13 CMIP6 (Coupled Model Intercomparison Project Phase 6) models.
• Data sources: Gridded datasets of annual peak runoff (proxy for flooding); complex network approach applied to these datasets.

Main Results

• CMIP6 models successfully reproduce major synchronization hotspots globally.
• CMIP6 models systematically overestimate network connectivity in 90% of regions and local clustering in 85% of regions.
• CMIP6 models underestimate the average link length in 70% of regions, resulting in overly connected networks dominated by short-range synchronization.
• The primary source of this structural bias is exaggerated temporal dependence driven by overly concentrated and shifted peak timing in the models.
• These emergent errors suggest a potential overestimation of spatially compound flood risk.
• The findings highlight deficiencies of climate models in reproducing global hydrological seasonality, particularly in snow-dominated regions.

Contributions

• Provides a global analysis of flood synchronization using gridded data and a complex network approach.
• Offers a comprehensive evaluation of CMIP6 models' performance in reproducing global flood synchronization patterns.
• Identifies systematic biases in CMIP6 models related to flood synchronization, connectivity, and link length.
• Pinpoints the underlying cause of these biases as exaggerated temporal dependence and shifted peak timing in models.
• Highlights critical implications for the assessment of spatially compound flood risk and for future climate model development, especially concerning hydrological seasonality.

Funding

Citation

@article{Yang2026Systematic,
  author = {Yang, Yixin and Villarini, Gabriele and Yang, Long},
  title = {Systematic Overestimation of Global Peak Runoff Synchronization in CMIP6 Models},
  journal = {Geophysical Research Letters},
  year = {2026},
  doi = {10.1029/2026gl122766},
  url = {https://doi.org/10.1029/2026gl122766}
}