Title: Statistical downscaling and projection of future temperatures across the Loess Plateau, China
Abstract: The Loess Plateau in China is one of the most erosive regions in the world, especially under warming climate conditions, which are aggravating evapotranspiration and water scarcity. Thus, there is a need to better understand historical and future climate change patterns in the Loess Plateau, and global climate models (GCMs) are a key tool to achieve this. Because there is a mismatch in spatial resolution between GCMs and the requirements of regional applications, the Statistical Downscaling Model (SDSM) in combination with two bias-correction methods was employed for the first time to downscale modeled values from Phase 5 of the Coupled Model Intercomparison Project for daily maximum temperature (TMAX), mean temperature (TMEAN), and minimum temperature (TMIN) over the Loess Plateau. After evaluation of model capability, the bias-corrected downscaled temperatures forced by GCM outputs for the period 2010–2099 were then projected. The results show that the combination of SDSM and the cumulative density function matching technique produced more accurate estimates than the integration of delta correction, and reduced root mean square errors (and associated standard deviations) by 59.2% (88.6%), 45.3% (78.8%), and 48.8% (43.4%) for TMAX, TMEAN, and TMIN, respectively. The projected results show that future temperatures will increase over the entire plateau relative to the 1961–1990 historical period, with the greatest changes in the northern and eastern regions. Another finding is that the downscaled models can reduce the uncertainties in projection to obtain more reliable future projections.