Dynamical downscaling simulation and projection for mean and extreme temperature and precipitation over central Asia

Abstract

As a typical arid and semi-arid area, central Asia (CA) has scarce water resources and fragile ecosystems that are particularly sensitive and vulnerable to climate change. In this study, dynamic downscaling was conducted to produce a regional dataset that incorporated the time period 1986–2100 for the CA. The results show that dynamic downscaling significantly improves the simulation for the mean and extreme climate over the CA, compared to the driving CCSM4 model. We show that significant warming will occur over CA with 2.0 °C and 5.0 °C increasing under the RCP4.5 and RCP8.5 scenarios, respectively by the end of twenty-first century. The daily maximum temperature, the daily minimum temperature and the annual total number of days with a minimum temperature greater than 25 °C will also increase significantly. The annual total number of days with a minimum temperature less than 0 °C will decrease significantly. Long-term trends in the projected winter precipitation under different emission scenarios exhibit robust and increasing changes during the twenty-first century, especially under the RCP8.5 scenario with an increasing about 0.1 mm/day. Significant differences are shown in the projection of precipitation-related indices over CA under different emission scenarios, and the impact of emissions is apparent for the number of days with ≥ 10 mm of precipitation, the density of precipitation on days with ≥ 1 mm of precipitation, and particularly for the maximum consecutive number of dry days that will increase significantly under the RCP8.5 scenario. Therefore, reduced greenhouse gases emissions have implications for mitigating extreme drought events over the CA in the future.