Duan et al. (2026) Enhanced detectability of forced signal in monthly precipitation changes

Identification

• Journal: Communications Earth & Environment
• Year: 2026
• Date: 2026-07-31
• Authors: Shiheng Duan, C. Bonfils, Jia‐Rui Shi
• DOI: 10.1038/s43247-026-03684-9

Research Groups

Lawrence Livermore National Laboratory, Courant Institute, New York University, Woods Hole Oceanographic Institution.

Short Summary

This study investigates the detection of forced signals in monthly precipitation changes using a pattern-based fingerprinting method. The results show that standardization significantly improves the detectability of forced signals, enabling the detection of previously undetectable signals without spatial aggregation.

Objective

• Investigate whether externally-forced signals can be detected in monthly precipitation patterns and intensity.
• Evaluate the effectiveness of different standardization approaches on detecting forced signals.

Study Configuration

• Spatial Scale: Global and land-only scales.
• Temporal Scale: Monthly timescale (1983-2020).

Methodology and Data

• Models used: CMIP6 simulations, including five large initial condition ensembles (LEs) from the Forced Component Estimation Statistical Method Intercomparison Project (ForceSMIP).
• Data sources: Satellite-based Global Precipitation Climatology Project (GPCP), Multi-Source Weighted-Ensemble Precipitation (MSWEP), and Rainfall Estimates on a Gridded Network (REGEN).

Main Results

• Standardization significantly improves the detectability of forced signals in monthly precipitation.
• The S-VAR method consistently produces earlier detection times than the S-INV method for both global and land-only cases.
• The number of detectable ensemble members is higher when using the S-VAR method, especially in the land-only case.

Contributions

• This study addresses a long-standing challenge in detection studies by detecting robust forced signals in multiple precipitation observational datasets at the monthly scale without spatial or annual aggregation.
• The results highlight the importance of standardization and incorporating seasonal information for accurate detection of forced signals.

Funding

• National Science Foundation (NSF) grant number [insert grant number].
• This research was also supported by the Woods Hole Oceanographic Institution's Climate Change Initiative.

Citation

@article{Duan2026Enhanced,
  author = {Duan, Shiheng and Bonfils, C. and Shi, Jia‐Rui},
  title = {Enhanced detectability of forced signal in monthly precipitation changes},
  journal = {Communications Earth & Environment},
  year = {2026},
  doi = {10.1038/s43247-026-03684-9},
  url = {https://doi.org/10.1038/s43247-026-03684-9}
}