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This study analyzed the temporal precipitation variations in the arid Central Asia (ACA) and their regional differences during 1930-2009 using monthly gridded precipitation from the Climatic Research Unit (CRU). Our results showed that the annual precipitation in this westerly circulation dominated arid region is generally increasing during the past 80 years, with an apparent increasing trend (0.7 mm/10 a) in winter. The precipitation variations in ACA also differ regionally, which can be divided into five distinct subregions (I West Kazakhstan region, II East Kazakhstan region, III Central Asia Plains region, IV Kyrgyzstan region, and V Iran Plateau region). The annual precipitation falls fairly even on all seasons in the two northern subregions (regions I and II, approximately north of 45°N), whereas the annual precipitation is falling mainly on winter and spring (accounting for up to 80% of the annual total precipitation) in the three southern subregions. The annual precipitation is increasing on all subregions except the southwestern ACA (subregion V) during the past 80 years. A significant increase in precipitation appeared in subregions I and III. The long-term trends in annual precipitation in all subregions are determined mainly by trends in winter precipitation. Additionally, the precipitation in ACA has significant interannual variations. The 2-3-year cycle is identified in all subregions, while the 5-6-year cycle is also found in the three southern subregions. Besides the inter-annual variations, there were 3-4 episodic precipitation variations in all subregions, with the latest episodic change that started in the mid-to late 1970s. The precipitations in most of the study regions are fast increasing since the late 1970s. Overall, the responses of ACA precipitation to global warming are complicated. The variations of westerly circulation are likely the major factors that influence the precipitation variations in the study region.arid Central Asia, annual and seasonal precipitation, changing tendency, regional difference Citation:

Abstract. Fine particulate matter with aerodynamic diameters ≤2.5 µm (PM2.5) has adverse effects on human health and the atmospheric environment. The estimation of surface PM2.5 concentrations has made intensive use of satellite-derived aerosol products. However, it has been a great challenge to obtain high-quality and high-resolution PM2.5 data from both ground and satellite observations, which is essential to monitor air pollution over small-scale areas such as metropolitan regions. Here, the space–time extremely randomized trees (STET) model was enhanced by integrating updated spatiotemporal information and additional auxiliary data to improve the spatial resolution and overall accuracy of PM2.5 estimates across China. To this end, the newly released Moderate Resolution Imaging Spectroradiometer Multi-Angle Implementation of Atmospheric Correction AOD product, along with meteorological, topographical and land-use data and pollution emissions, was input to the STET model, and daily 1 km PM2.5 maps for 2018 covering mainland China were produced. The STET model performed well, with a high out-of-sample (out-of-station) cross-validation coefficient of determination (R2) of 0.89 (0.88), a low root-mean-square error of 10.33 (10.93) µg m−3, a small mean absolute error of 6.69 (7.15) µg m−3 and a small mean relative error of 21.28 % (23.69 %). In particular, the model captured well the PM2.5 concentrations at both regional and individual site scales. The North China Plain, the Sichuan Basin and Xinjiang Province always featured high PM2.5 pollution levels, especially in winter. The STET model outperformed most models presented in previous related studies, with a strong predictive power (e.g., monthly R2=0.80), which can be used to estimate historical PM2.5 records. More importantly, this study provides a new approach for obtaining high-resolution and high-quality PM2.5 dataset across mainland China (i.e., ChinaHighPM2.5), important for air pollution studies focused on urban areas.

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