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Advances in Meteorology
Volume 2014 (2014), Article ID 269059, 8 pages
Research Article

Multivariate Regression Analysis and Statistical Modeling for Summer Extreme Precipitation over the Yangtze River Basin, China

Tao Gao1,2,3 and Lian Xie1,3

1Key Laboratory of Marine Environment and Ecology (Ocean University of China), Ministry of Education of China, Qingdao 266100, China
2Department of Resources and Environmental Sciences, Heze University, Heze 274000, China
3Department of Marine, Earth and Atmospheric Sciences, North Carolina State University, Raleigh, NC 27695, USA

Received 22 November 2013; Accepted 4 December 2013; Published 8 January 2014

Academic Editor: Huiwang Gao

Copyright © 2014 Tao Gao and Lian Xie. 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.


Extreme precipitation is likely to be one of the most severe meteorological disasters in China; however, studies on the physical factors affecting precipitation extremes and corresponding prediction models are not accurately available. From a new point of view, the sensible heat flux (SHF) and latent heat flux (LHF), which have significant impacts on summer extreme rainfall in Yangtze River basin (YRB), have been quantified and then selections of the impact factors are conducted. Firstly, a regional extreme precipitation index was applied to determine Regions of Significant Correlation (RSC) by analyzing spatial distribution of correlation coefficients between this index and SHF, LHF, and sea surface temperature (SST) on global ocean scale; then the time series of SHF, LHF, and SST in RSCs during 1967–2010 were selected. Furthermore, other factors that significantly affect variations in precipitation extremes over YRB were also selected. The methods of multiple stepwise regression and leave-one-out cross-validation (LOOCV) were utilized to analyze and test influencing factors and statistical prediction model. The correlation coefficient between observed regional extreme index and model simulation result is 0.85, with significant level at 99%. This suggested that the forecast skill was acceptable although many aspects of the prediction model should be improved.