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A Stepwise Regression and Statistical Downscaling Approach for Projecting Temperature Variations under Multiple RCP Scenarios

X. Huang1, X. Zhou1 *, G. H. Huang1, Y. P. Li1, and Y. F. Li1

  1. State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China

*Corresponding author. Tel.: +86 15122694282. E-mail address: (X. Zhou).


With the rapid development of Central China, the temperature in this region is continuously increasing. Extreme weather events (e.g., high-temperature weather for many consecutive days) are becoming frequent. In order to provide future theoretical guidance on the direction of local development and the prevention of extreme natural disasters, the daily datasets of 12 meteorological stations in three provinces were collected. The corresponding predictors from 25 large-scale climatic factors were then screened using stepwise regression. A stepwise regression and statistical downscaling (SRSD) approach was developed to establish the statistical relationship. The future temperature results were projected by the weather generator, and the probability of extreme weather occurrence was analyzed by extreme values. The results indicate that future temperature in Central China shows an increasing trend from 2036 to 2065 and 2066 to 2095, with the representative concentration pathway 4.5 (RCP4.5) scenario showing a greater increase in temperature than the representative concentration pathway 8.5 (RCP8.5) scenario. Hunan Province has the largest temperature increase, followed by Hubei Province and Henan Province. The average annual duration of heat waves in Central China is 74.7 days.

Keywords: climate change scenarios, statistical downscaling, central China, stepwise regression and statistical downscaling

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