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

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


In this study, we use four statistical downscaling methods to statistically downscale seven Coupled Model Intercomparison Project (CMIP5) Global Climate Models (GCMs) and project the changes in precipitation and temperature over China under RCP4.5 and RCP8.5 emission scenarios. The four statistical downscaling methods are bias-correction and spatial downscaling (BCSD), bias-correction and climate imprint (BCCI), bias correction constructed analogues with quantile mapping reordering (BCCAQ), and cumulative distribution function transform (CDF-t). Though large inter-model variability exists in the distribution and magnitude of changes in projected precipitation, particularly for wet spell length (CWD), all downscaling methods generally project a consistent enhancement of precipitation in both summer and winter over most parts of China. For the arid and semiarid Northwest China, the shortened dry spell length (CDD) is accompanied by the pronouncedly intensified very wet days (R95p), as well as the increase in maximum 5-day precipitation amount (Rx5day). In contrast, southeastern regions may experience more consecutive dry days and more severe wet precipitation extremes. The projected changes from different downscaling techniques are fairly similar for temperature, apart from the diurnal temperature range for BCSD. Warming is projected across the whole domain with larger magnitude over the north and in winter under RCP8.5. More summer days and fewer frost days would appear in the future. The bias correction components of downscaling methods cause a higher degree of agreement among models, and the downscaled results generally retain the main climate change signal of the driving models.


Statistical downscaling Climate change Intercomparison China Extreme 



This work is supported by the National Key Research and Development Program of China (2017YFA0603803 and 2016YFC0202000: Task 2) and the National Natural Science Foundation of China (91425304, 41575099 and 41275004). This work is also supported by the Chinese Jiangsu Collaborative Innovation Center for Climate Change. The authors sincerely appreciate the National Climate Center of China Meteorological Administration for providing the high-resolution gridded observations applied in this study. The CMIP5 dataset is provided by the World Climate Research Programme, whose support is gratefully acknowledged. Special thanks to the free R software, which enables convenient and time-saving calculations.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

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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