Zhuo et al. (2026) A physically constrained multivariate bias correction framework for projecting compound drought and heatwave risk in the Yangtze River Basin

Identification

• Journal: Journal of Hydrology
• Year: 2026
• Date: 2026-09-23
• Authors: Yue Zhuo, Zhiyong Wu, Hai He, Salvatore Pascale, Zhenchen Liu, Yuqing Feng, Yangqian Li, Shuhao Mei
• DOI: 10.1016/j.jhydrol.2026.136475

Research Groups

• College of Hydrology and Water Resources, Hohai University, Nanjing, China
• State Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, China
• Department of Physics and Astronomy, University of Bologna, Bologna, Italy
• Yellow River Engineering Consulting Co., Ltd., Zhengzhou, China

Short Summary

This study developed a physically constrained, dependence-preserving multivariate bias-correction framework (integrating CycleGAN, BiLSTM, and QDM) to jointly correct daily precipitation and temperature from 13 CMIP6 models. The framework improved the representation of compound drought and heatwave (CDHW) dependence and historical spatial patterns, revealing that projected CDHW occurrence depends critically on the heatwave threshold definition.

Objective

• To develop and apply a physically constrained, dependence-preserving multivariate bias-correction framework to jointly correct daily precipitation, maximum temperature (Tmax), and minimum temperature (Tmin) from CMIP6 models for reliable projections of compound drought and heatwave (CDHW) risk in the Yangtze River Basin, addressing biases and distorted dependencies in conventional methods.

Study Configuration

• Spatial Scale: Yangtze River Basin
• Temporal Scale: Daily (data frequency); Historical period (for calibration/validation); Future projections (e.g., SSP5-8.5).

Methodology and Data

• Models used: CycleGAN, BiLSTM, and Quantile Delta Mapping (QDM) for multivariate bias correction; 13 CMIP6 global climate models.
• Data sources: CMIP6 climate model simulations (daily precipitation, maximum temperature, minimum temperature); Observational data (for bias correction).

Main Results

• The developed framework improved the representation of compound drought and heatwave (CDHW)-relevant hot-dry dependence.
• It reproduced the observed spatial pattern of historical normalized cumulative CDHW intensity with a correlation coefficient of 0.64.
• Quantile Delta Mapping (QDM) compensation improved the preservation of the raw CMIP6-projected maximum temperature warming signal.
• The CycleGAN–BiLSTM component reduced maximum temperature–precipitation dependence errors compared to QDM alone.
• Under a fixed historical-reference heatwave threshold, projected CDHW occurrence increased, particularly under the SSP5-8.5 scenario, mainly because more days exceeded the historical heat threshold.
• In contrast, under period-adaptive heatwave thresholds, CDHW occurrence conditional on concurrent heatwave and drought did not increase relative to the historical period.
• Sensitivity analyses demonstrated that the magnitude and interpretation of projected CDHW changes depended significantly on the heatwave-threshold definition and the formulation of potential evapotranspiration.

Contributions

• Development of a novel physically constrained, dependence-preserving multivariate bias-correction framework that integrates CycleGAN, BiLSTM, and Quantile Delta Mapping (QDM).
• Addressing the critical limitation of conventional univariate bias correction methods by preserving the joint dependence of precipitation and temperature in CMIP6 models.
• Providing more reliable projections of compound drought and heatwave risk by accurately representing the complex joint behavior of these variables.
• Highlighting the crucial influence of heatwave-threshold definition and potential evapotranspiration formulation on the interpretation and magnitude of projected CDHW changes.

Funding

• The provided text does not contain explicit funding information.

Citation

@article{Zhuo2026physically,
  author = {Zhuo, Yue and Wu, Zhiyong and He, Hai and Pascale, Salvatore and Liu, Zhenchen and Feng, Yuqing and Li, Yangqian and Mei, Shuhao},
  title = {A physically constrained multivariate bias correction framework for projecting compound drought and heatwave risk in the Yangtze River Basin},
  journal = {Journal of Hydrology},
  year = {2026},
  doi = {10.1016/j.jhydrol.2026.136475},
  url = {https://doi.org/10.1016/j.jhydrol.2026.136475}
}