An intercomparison of multiple statistical downscaling methods for daily precipitation and temperature over China: present climate evaluations

  • Yi Yang
  • Jianping TangEmail author
  • Zhe Xiong
  • Shuyu Wang
  • Jian Yuan


Four statistical downscaling methods, that is, three quantile mapping based techniques including bias-correction and spatial downscaling (BCSD), bias-correction and climate imprint (BCCI), and bias correction constructed analogues with quantile mapping reordering (BCCAQ), and the cumulative distribution function transform (CDF-t) method, are evaluated with daily observed precipitation and surface temperature for 1961–2005 over China. The four downscaling methods improve the accuracy over the driving general climate models (GCMs) significantly in terms of spatial variability, bias, seasonal cycle, and probability density functions of daily series and extreme events. Overall, BCSD outperforms other methods in frequency distributions of daily temperature, precipitation, and extreme precipitation indices such as wet and dry spell lengths. But it comparably has larger biases in temperature-related extremes. When downscaling the seasonal and extreme precipitation, the three quantile mapping based techniques exhibit better capacity than CDF-t in terms of the spatial correlation and bias over all subregions. Whereas CDF-t methods overestimate consecutive wet days and extreme wet indices significantly, as it displays limited improvement over the driving GCMs by producing too many drizzle days using either absolute or relative threshold. All methods are equally skillful in downscaling monthly and seasonal temperature, and the temperature extremes are better reproduced by BCCI, BCCAQ and CDF-t. However, the statistical downscaling methods show limited capacity in improving the interannual variability of temperature and precipitation extremes.


Statistical downscaling Intercomparison China Extreme 



The work is jointly funded by the National Key Research and Development Program of China (2018YFA0606003 and 2016YFC0202000: Task 2) and the National Natural Science Foundation of China (41875124, 91425304, 41575099 and 41275004). This work is also supported by the Chinese Jiangsu Collaborative Innovation Center for Climate Change. The authors would like to thank National Climate Center of China Meteorological Administration for the provision of high-resolution gridded observations (CN05.1), the World Climate Research Programme ( for providing the CMIP5 data, and Michelangeli et al. (2009) for developing and making available the R-package “CDFt”. The statistical downscaling in this paper has been performed on the computing facilities in the High Performance Computing Center (HPCC) of Nanjing University.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.CMA-NJU Joint Laboratory for Climate Prediction Studies, Institute for Climate and Global Change Research, School of Atmospheric SciencesNanjing UniversityNanjingChina
  2. 2.Key Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  3. 3.Institute for Climate and Global Change Research, School of Atmospheric SciencesNanjing UniversityNanjingChina

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