Application of tree-structured regression for regional precipitation prediction using general circulation model output

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16 Scopus citations


This study presents a tree-structured regression (TSR) method to relate daily precipitation with a variety of free-atmosphere variables. Historical data were used to identify distinct weather patterns associated with differing types of precipitation events. Models were developed using 67% of the data for training and the remaining data for model validation. Seasonal models were built for each of 2 US sites: San Francisco, California, and San Antonio, Texas. The average correlation between observed and simulated daily precipitation data series is 0.75 for the training set and 0.68 for the validation set. Relative humidity was found to be the dominant variable in these TSR models. Output from an NCAR CSM (climate system model) transient simulation of climate change were then used to drive the TSR models in the prediction of precipitation characteristics under climate change. A preliminary screening of the GCM output variables for current climate, however, revealed significant problems for the San Antonio site. Specifically, the CSM missed the annual trends in humidity for the grid cell containing this site. CSM output for the San Francisco site was found to be much more reliable. Therefore, we present future precipitation estimates only for the San Francisco site.

Original languageEnglish (US)
Pages (from-to)17-30
Number of pages14
JournalClimate Research
Issue number1
StatePublished - Nov 10 2000
Externally publishedYes



  • Climate change
  • Downscaling
  • General circulation models (GCMs)
  • Precipitation
  • Regional climate

ASJC Scopus subject areas

  • Environmental Chemistry
  • Environmental Science(all)
  • Atmospheric Science

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