Yang et al. (2026) Forewarning extreme precipitation events using scaling behaviors

Identification

• Journal: Scientific Reports
• Year: 2026
• Date: 2026-03-28
• Authors: Lichao Yang, Naiming Yuan, Boyu Chen
• DOI: 10.1038/s41598-026-45565-3

Research Groups

• College of Resource Environment and Tourism, Capital Normal University, Beijing, China
• School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China
• National Meteorological Center, China Meteorological Administration, Beijing, China

Short Summary

This study investigates the temporal clustering of hourly extreme precipitation in Eastern China using scaling properties to improve forecasting capabilities. It finds that hourly extreme precipitation events tend to occur in clusters, unlike daily extremes, and proposes a scaling-behavior-based forewarning method that shows comparable performance to dynamical forecast models.

Objective

• To gain a deeper understanding of the fine-scale temporal structure and evolution mechanisms of hourly extreme precipitation events.
• To investigate the temporal clustering behavior of hourly extreme precipitation and explore its underlying mechanisms through the scaling properties of hourly precipitation.

Study Configuration

• Spatial Scale: Eastern China
• Temporal Scale: Hourly

Methodology and Data

• Models used: A method for forewarning extreme precipitation based on inherent scaling behavior; compared against dynamical forecast models.
• Data sources: Observed hourly precipitation data.

Main Results

• Unlike daily extreme precipitation, hourly extreme precipitation events tend to occur in clusters.
• Time series exhibiting similar scaling properties of precipitation can effectively reproduce these observed clusters.
• A novel method for forewarning extreme precipitation, based on their inherent scaling behavior, was developed and demonstrated comparable forewarning capabilities to existing dynamical forecast models.

Contributions

• Deepens the understanding of the mechanisms underlying hourly extreme precipitation.
• Provides valuable insights for improving the simulation and early warning capabilities for regional hourly extreme precipitation.
• Introduces a new method for forewarning extreme precipitation based on scaling behaviors, offering a complementary approach to traditional dynamical models.

Funding

• National Natural Science Foundation of China (No. 42105061, 42330111)
• The Guangdong Basic and Applied Basic Research Foundation (Grant No. 2023B1515020084)
• National Key Scientific and Technological Infrastructure project “Earth System Numerical Simulation Facility” (EarthLab)

Citation

@article{Yang2026Forewarning,
  author = {Yang, Lichao and Yuan, Naiming and Chen, Boyu},
  title = {Forewarning extreme precipitation events using scaling behaviors},
  journal = {Scientific Reports},
  year = {2026},
  doi = {10.1038/s41598-026-45565-3},
  url = {https://doi.org/10.1038/s41598-026-45565-3}
}