Abstract
Two approaches of statistical downscaling were applied to indices of temperature extremes based on percentiles of daily maximum and minimum temperature observations at Beijing station in summer during 1960–2008. One was to downscale daily maximum and minimum temperatures by using EOF analysis and stepwise linear regression at first, then to calculate the indices of extremes; the other was to directly downscale the percentile-based indices by using seasonal large-scale temperature and geo-potential height records. The cross-validation results showed that the latter approach has a better performance than the former. Then, the latter approach was applied to 48 meteorological stations in northern China. The cross-validation results for all 48 stations showed close correlation between the percentile-based indices and the seasonal large-scale variables. Finally, future scenarios of indices of temperature extremes in northern China were projected by applying the statistical downscaling to Hadley Centre Coupled Model Version 3 (HadCM3) simulations under the Representative Concentration Pathways 4.5 (RCP 4.5) scenario of the Fifth Coupled Model Inter-comparison Project (CMIP5). The results showed that the 90th percentile of daily maximum temperatures will increase by about 1.5°C, and the 10th of daily minimum temperatures will increase by about 2°C during the period 2011–35 relative to 1980–99.
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Fan, L., Chen, D., Fu, C. et al. Statistical downscaling of summer temperature extremes in northern China. Adv. Atmos. Sci. 30, 1085–1095 (2013). https://doi.org/10.1007/s00376-012-2057-0
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DOI: https://doi.org/10.1007/s00376-012-2057-0