Central Asia, a typical arid and semi‐arid area, is very sensitive to climate change and is projected to undergo vigorous warming trend in future. However, it is still unclear how extreme temperatures have changed. Therefore, using the daily maximum and minimum temperatures collected from 108 stations in the study area from 1981 to 2015, the spatial–temporal variations in extreme temperature in Central Asia were analysed on both annual and seasonal scales based on 11 indices from The Expert Team on Climate Change Detection and Indices. Results show that: (1) from 1981 to 2015, all extreme temperature values exhibited increasing trends on an annual scale, but maximum temperature (TMAX) increased faster than the minimum temperature (TMIN), leading to an overall increase in the diurnal temperature range (DTR). Most of the stations in Central Asia, except for those in Xinjiang (China), exhibited an increasing trend in DTR, while most of the stations in Xinjiang (China) exhibited reversely significant decreasing trends. (2) TMAX, TMIN and DTR increased significantly, mainly during spring and summer. The trends of most warm extreme indices (warm days, warm nights and warmest days) indicate that the warming mainly occurred in spring, while most cold extreme indices (cool days, cool nights and coldest nights) exhibited significant warming trends in autumn. Dominant warming during spring and autumn can lead to longer summers, which may further accelerate the frequencies and magnitudes of temperature extremes. (3) Additionally, TMAX, TMIN and the percentile‐based warm indices all exhibited significant increasing trends from southwestern Central Asia (Turkmenistan) to eastern Central Asia (Xinjiang (China)) via the Tianshan Mountains. Although the percentile‐based cold indices showed decreasing trends in this region, they were too minor to offset the overall warming trend. With longer summers, stronger and more prolonged melting seasons were detected in the mountains of Central Asia.

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Temporal and spatial changes in vegetation and their influencing factors are of great significance for the assessment of climate change and sustainable development of ecosystems. This study applied the Asymmetric Gaussians (AG) fitting method, Mann-Kendall test, and correlation analysis to the Global Inventory Monitoring and Modeling System (GIMMS) third-generation Normalized Difference Vegetation Index and gridded climate and drought data for 1982–2015. The temporal and spatial changes to NDVI for natural grassland and forest during the growing season were analyzed. Relationships among NDVI, climate change, and droughts were also analyzed to reveal the influence of vegetation change. The results showed that: (1) Land use/cover change (LUCC) in China was mainly represented by increases in agricultural land (Agrl) and urban and rural land (Uril), and decreases in unutilized land (Bald), grassland, forest, and permanent glacier and snow (Snga). The increase in agricultural land was mainly distributed in the western northwest arid area (WNW) and northern North China (NNC), whereas regions with severe human activities such as southern South China (SNC), western South China (WSC), and eastern South China (ESC) showed significant decreases in agricultural land due to conversion to urban and rural land. (2) The start of the growing season (SOS) was advanced in WNW, SNC, WSC, and ESC, and the end of growing season (EOS) was delayed in WNW, NNC, and SNC. The growing season length (GSL) of natural vegetation in China has been extended by eight days over the last 34 years. However, the phenology of the Qinghai-Tibet Plateau (TP) was opposite to that of the other regions and the GSL showed an insignificant decreasing trend. (3) The NDVI increased significantly, particularly in the SNC, WSC, ESC, and the grassland of the WNW. Precipitation was found to mainly control the growth of vegetation in the arid and semi-arid regions of northwest China (WNW and ENW), and precipitation had a much greater impact on grassland than on forests. Temperature had an impact on the growth of vegetation throughout China, particularly in SNC, ESC, and WSC. (4) The Standardized Precipitation Evapotranspiration Index (SPEI) showed a downward trend, indicating an aridification trend in China, particularly in ENW, NNC, and WNW. Similar to precipitation, the main areas affected by drought were WNW and ENW and grassland was found to be more sensitive to drought than forest. The results of this study are of great significance for predicting the response of ecosystem productivity to climate change under future climate change scenarios.