Bivariate Downscaling With Asynchronous Measurements

Xuming He, Yunwen Yang, Jingfei Zhang

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Statistical downscaling is a useful technique to localize global or regional climate model projections to assess the potential impact of climate changes. It requires quantifying a relationship between climate model output and local observations from the past, but the two sets of measurements are not necessarily taken simultaneously, so the usual regression techniques are not applicable. In the case of univariate downscaling, the Statistical Asynchronous Regression (SAR) method of O'Brien, Sornette, and McPherron (Journal of Geophysical Research, 106, 13247-13259, 2001) provides a simple quantile-matching approach with asynchronous measurements. In this paper, we propose a bivariate downscaling method for asynchronous measurements based on a notion of bivariate ranks and positions. The proposed method is preferable to univariate downscaling, because it is able to preserve general forms of association between two variables, such as temperature and precipitation, in statistical downscaling. This desirable property of the bivariate downscaling method is demonstrated through applications to simulated and real data.

Original languageEnglish (US)
Pages (from-to)476-489
Number of pages14
JournalJournal of Agricultural, Biological, and Environmental Statistics
Issue number3
StatePublished - Sep 1 2012
Externally publishedYes


  • Asynchronous regression
  • Bivariate ranks
  • Climate
  • Quantile
  • Statistical downscaling

ASJC Scopus subject areas

  • Statistics and Probability
  • Agricultural and Biological Sciences (miscellaneous)
  • Environmental Science(all)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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