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Advances in Meteorology
Volume 2017 (2017), Article ID 7265178, 11 pages
Research Article

Statistical Downscaling of Temperature with the Random Forest Model

Bo Pang,1,2 Jiajia Yue,1,2 Gang Zhao,1,2 and Zongxue Xu1,2

1College of Water Sciences, Beijing Normal University, Beijing 100875, China
2Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing 100875, China

Correspondence should be addressed to Gang Zhao

Received 20 December 2016; Revised 25 March 2017; Accepted 17 May 2017; Published 15 June 2017

Academic Editor: Jorge E. Gonzalez

Copyright © 2017 Bo Pang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The issues with downscaling the outputs of a global climate model (GCM) to a regional scale that are appropriate to hydrological impact studies are investigated using the random forest (RF) model, which has been shown to be superior for large dataset analysis and variable importance evaluation. The RF is proposed for downscaling daily mean temperature in the Pearl River basin in southern China. Four downscaling models were developed and validated by using the observed temperature series from 61 national stations and large-scale predictor variables derived from the National Center for Environmental Prediction–National Center for Atmospheric Research reanalysis dataset. The proposed RF downscaling model was compared to multiple linear regression, artificial neural network, and support vector machine models. Principal component analysis (PCA) and partial correlation analysis (PAR) were used in the predictor selection for the other models for a comprehensive study. It was shown that the model efficiency of the RF model was higher than that of the other models according to five selected criteria. By evaluating the predictor importance, the RF could choose the best predictor combination without using PCA and PAR. The results indicate that the RF is a feasible tool for the statistical downscaling of temperature.