Yu et al. (2026) A tri‑basin matching index improves Indian Summer Monsoon Rainfall predictability

Identification

• Journal: npj Climate and Atmospheric Science
• Year: 2026
• Date: 2026-09-29
• Authors: Shiyun Yu, Rong‐Hua Zhang, Lei Fan
• DOI: 10.1038/s41612-026-01553-y

Research Groups

• State Key Laboratory of Climate System Prediction and Risk Management/Key Laboratory of Meteorological Disaster, Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, China (Shi-Yun Yu & Rong-Hua Zhang)
• School of Atmospheric Sciences, Nanjing University of Information Science and Technology, China (Shi-Yun Yu & Rong-Hua Zhang)
• Physical Oceanography Laboratory/Collaborative Innovation Center of Marine Science and Technology, Ocean University of China, Qingdao, China (Lei Fan)
• College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, China (Lei Fan)

Short Summary

This study proposes a Matching Index (MI) framework to improve Indian Summer Monsoon Rainfall predictability by identifying state-dependent ocean–monsoon coupling regimes. The MI framework improves ISMR reconstruction correlation from 0.73 using conventional multivariate linear regression to 0.83.

Objective

• Identify the principal hypothesis: Interannual variability of Indian Summer Monsoon Rainfall (ISMR) is vital for South Asian water resources and agriculture, but its prediction is challenged by non-stationary ocean–monsoon interactions.

Study Configuration

• Spatial Scale: Regional scale over India
• Temporal Scale: Interannual variability

Methodology and Data

• Models used: Matching Index (MI) framework combined with Empirical Orthogonal Function analysis (EOF)
• Data sources: Satellite, observation, reanalysis data for ISMR and sea surface temperature anomalies

Main Results

• Three dominant regimes of ocean–monsoon coupling are identified: ENSO intensity, ENSO lifecycle, and ENSO spatial patterns.
• The MI framework improves ISMR reconstruction correlation from 0.73 to 0.83.

Contributions

• This study provides a physically based approach for diagnosing evolving air–sea coupling and improving monsoon prediction under climate change.
• The results demonstrate that ISMR variability is fundamentally state dependent and cannot be described by stationary linear relationships.

Funding

• Jiangsu Funding Program for Excellent Postdoctoral Talent (2024ZB012)
• Post-doctoral Fellowship Program of CPSF under Grant Number GZC20240736
• National Natural Science Foundation of China (grant no. 42030410, grant no. LSKJ202202402, and grant no. JSSCTD202346)

Citation

@article{Yu2026tribasin,
  author = {Yu, Shiyun and Zhang, Rong‐Hua and Fan, Lei},
  title = {A tri‑basin matching index improves Indian Summer Monsoon Rainfall predictability},
  journal = {npj Climate and Atmospheric Science},
  year = {2026},
  doi = {10.1038/s41612-026-01553-y},
  url = {https://doi.org/10.1038/s41612-026-01553-y}
}