EGU24-4810, updated on 08 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Dynamical Downscaling Simulation of Asian Climate with a Bias-Corrected CMIP6 Dataset: Evaluation 

Zhongfeng Xu1, Ying Han1, Meng-Zhuo Zhang2, Chi-Yung Tam3, Zong-Liang Yang4, Ahmed EL Kenawy5,6, and Congbin Fu2
Zhongfeng Xu et al.
  • 1Institute of Atmospheric Physics, Chinese Academy of Sciences, Key Laboratory of Regional Climate-Environment Research for Temperate East Asia, Beijing, China (
  • 2School of Atmospheric Sciences, Nanjing University, Nanjing, 210093, China
  • 3Earth System Science Programme, The Chinese University of Hong Kong, Hong Kong, China
  • 4Department of Geological Sciences, Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas, USA
  • 5Instituto Pirenaico de Ecología, Consejo Superior de Investigaciones Científicas (CSIC), , Campus de Aula Dei, 1005, Zaragoza, 50059, Spain
  • 6Department of Geography, Mansoura University, Mansoura, 35516, Egypt

    In this study, we aim to assess the impacts of GCM bias correction on dynamical downscaling simulation over the Asia-western North Pacific region. Three simulations were conducted with a 25-km grid spacing for the period 1980–2014. The first simulation (WRF_ERA5) was driven by the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) dataset and served as the validation dataset. The original GCM dataset (MPI-ESM1-2-HR model) was used to drive the second simulation (WRF_GCM), while the third simulation (WRF_GCMbc) was driven by the bias-corrected GCM dataset. The bias-corrected GCM data has an ERA5-based mean and interannual variance but the long-term trends are derived from the ensemble mean of 18 CMIP6 models. Results demonstrate that the WRF_GCMbc significantly reduced the root-mean-square errors (RMSEs) of the climatological mean of downscaled variables, including temperature, precipitation, snow, wind, relative humidity, and planetary boundary layer height by 50%–90% compared to the WRF_GCM. Similarly, the RMSEs of interannual-to-interdecadal variances of downscaled variables were reduced by 30%–60%. Furthermore, the WRF_GCMbc better captured the annual cycle of the monsoon circulation and intraseasonal and day-to-day variabilities. The leading empirical orthogonal function (EOF) shows a monopole precipitation mode in the WRF_GCM. In contrast, the WRF_GCMbc successfully reproduced the observed tri-pole mode of summer precipitation over eastern China. This improvement could be attributed to a better-simulated location of the western North Pacific subtropical high in the WRF_GCMbc after GCM bias correction.

How to cite: Xu, Z., Han, Y., Zhang, M.-Z., Tam, C.-Y., Yang, Z.-L., EL Kenawy, A., and Fu, C.: Dynamical Downscaling Simulation of Asian Climate with a Bias-Corrected CMIP6 Dataset: Evaluation , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4810,, 2024.