摘要
The long-term goal of the 2015 Paris Agreement is to limit global warming to well below 2 ℃above pre-industrial levels and to pursue efforts to limit it to 1.5 ℃. However, for climate mitigation and adaption efforts, further studies are still needed to understand the regional consequences between the two global warming limits. Here we provide an assessment of changes in temperature extremes over China （relative to 1986-2005） at 1.5 ℃ and 2 ℃ warming levels （relative to 1861-1900） by using the 5th phase of the Coupled Model Intercomparison Project （CMIP5） models under three RCP scenarios （RCP2.6, RCP4.5, RCP8.5）. Results show that the increases in mean temperature and temperature extremes over China are greater than that in global mean temperature. With respect to 1986-2005, the temperature of hottest day （TXx） and coldest night （TNn） are projected to increase about 1/1.6 ℃ and 1.1/1.8 ℃, whereas warm days （TX90p） and warm spell duration （WSDI） will increase about 7.5/13.8% and 15/30 d for the 1.5/2 ℃ global warming target, respectively. Under an additional 0.5 ℃ global warming, the projected increases of temperature in warmest day/night and coldest day/night are both more than 0.5 ℃ across almost the whole China. In Northwest China, Northeast China and the Tibetan Plateau, the projected changes are particularly sensitive to the additional 0.5 ℃ global warming, for example, multi-model mean increase in coldest day （TXn） and coldest night （TNn） will be about 2 times higher than a change of 0.5 ℃ global warming. Although the area-averaged changes in temperature extremes are very similar for different scenarios, spatial hotspot still exists, such as in Northwest China and North China, the increases in temperatures are apparently larger in RCP8.5 than that in RCP4.5.
基金
We acknowledge the World Climate Research Programme＇s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups （listed in Table l） for producing and making available their model output. This research is supported by the National Key Research and Development Program of China （2017YFA0603804） and the State Key Program of National Natural Science Foundation of China （41230528）.
