EGU22-3424, updated on 27 Mar 2022
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Multivariate bias corrections of global compound dry and hot events in CMIP6 model simulations 

Yu Meng and Zengchao Hao
Yu Meng and Zengchao Hao
  • College of Water Sciences, Beijing Normal University, Beijing, China (

Climate extremes induced by global warming have remarkable impacts on water resources, agricultural production, and terrestrial ecosystems. Climatic model simulations provide useful information to analyze changes in extremes (e.g., droughts, heatwaves) under global warming for climate policies and mitigation measures. However, systematic biases exist in climate model simulations, which hinders accurate assessments of extremes changes. Bias correction methods have been employed to correct biases in climate variables (e.g., precipitation, temperature) in model simulations. Previous studies mostly focus on individual variables while the correction of inter-variable correlation (e.g., precipitation-temperature dependence) is still limited. Moreover, the concurrence of climate extremes (e.g., droughts and hot extremes), which is closely related to the dependence among contributing variables, may amplify the impacts. However, bias correction of the contributing variables of compound events is still limited but growing. In this study, we employ the multivariate bias correction (MBC) approach to correct the precipitation, temperature, and their dependence from CMIP6 simulations. We found that the MBC can improve the simulation of precipitation-temperature dependence and associated compound dry and hot events. This study can provide useful insights for improving model simulations of compound weather and climate extremes for impact studies and mitigation measures.

How to cite: Meng, Y. and Hao, Z.: Multivariate bias corrections of global compound dry and hot events in CMIP6 model simulations , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3424,, 2022.