中国幅员辽阔、气候差异大、人口众多,在全球气候变化背景下研究中国的气候变化对维护中国乃至世界的粮食安全以及社会经济持续稳定发展具有十分重要的意义。地表温度最大值可避免云雨天气对热红外遥感获取地表温度准确数据的干扰,且年变化较为稳定,但对重大土地利用转移、干旱热浪等高度敏锐。基于2003-2018年MODIS（Moderate resolution imaging spectroradiometer）地表温度产品,采用气候倾向率、线性相关系数等研究方法分析了中国地表温度年最大值的时空分布及变化特征。结果表明：（1） 中国地表温度年最大值呈现北高南低的空间分布特征,中国地表温度年最大值的最高值位于新疆吐鲁番盆地。（2） 地表温度年最大值的空间分布按中国气候区划分区分析表明,温带大陆性气候区地表温度年最大值最高,温带季风气候区的值较高,高原山地气候区、亚热带季风气候区、热带季风气候区的值较低,地表温度年最大值的空间分布格局与地表覆盖类型相关。（3） 2003-2018年,中国地表温度年最大值的时间变化特征总体表现为微弱降温趋势,气候倾向率为-0.06 K·(10a)^-1,降温区域占全国总面积的50.45%。（4） 气候倾向率在空间上表现为西高东低,西部升温趋势比东部更加明显。部分区域表现为显著降温趋势,如北方中部、华南区域、塔里木盆地边缘等,这些区域的降温与植被覆盖变化有关。

Land surface temperature (LST) is a very important environmental factor that impacts the energy and water exchange between the atmosphere and ecosystems. Research on China’s climate change in the context of global climate change is of great significance for global and Chinese food security as well as the sustained and stable development of the social economy. Compared with surface temperature data obtained via thermal infrared remote sensing, information regarding the maximum surface temperature is of increased significance as it removes the influence of clouds and rain. The annual variation of the maximum surface temperature is relatively stable; however, it is highly sensitive to changes in land use, drought, and heat waves. This study uses moderate resolution imaging spectroradiometer (MODIS) surface temperature data and normalized difference vegetation index (NDVI) data to obtain the annual maximum surface temperature using the maximum value synthesis method. On the basis of the MODIS LST data from 2003 to 2018, the temporal and spatial distribution and variation characteristics of annual LST maxima in China were analyzed using techniques including the climate trend rate and linear correlation coefficient, and regional analyses were conducted according to the agricultural climate regionalization of China. The correlation between the annual maximum change of the LST and land cover types in China is established. The correlation between the annual maximum LST and the maximum NDVI in the regions with significant trend changes is studied, and the factors that influence the annual maximum LST change in China are investigated. The results obtained indicate the following: (1) The annual maximum value of China’s surface temperature presents a higher spatial distribution characteristic in the north and lower in the south; its highest value was observed in the Turpan Basin of Xinjiang. (2) According to the analysis of the spatial distribution of the annual maximum surface temperature in China’s climate zoning, the annual maximum surface temperature is the highest in the temperate continental climate zone, with an average of 325.39 K over 16 years. The second hottest region is the temperate monsoon climate zone, where the average annual maximum surface temperature over 16 years is 312.84 K. The plateau mountain, subtropical monsoon, and tropical monsoon climate zones have lower values of annual maximum surface temperature. The spatial distribution pattern of the annual maximum surface temperature is related to the type of surface cover. (3) During the period of 2003-2018, the temporal variation of the annual maximum surface temperature in China showed a slight cooling trend, with a climate tendency rate of -0.06 K·(10a)^-1; the area in which a cooling trend was observed accounted for 50.45% of the country’s total area. (4) The climate tendency rate of the annual maximum surface temperature and the linear correlation coefficient are high in the west and low in the east, and the warming trend in the west is clearer than that in the east. Some areas showed a significant cooling trend; these areas included central northern China, southern China, and the edge of the Tarim Basin. The cooling in these areas is related to changes in the vegetation cover. The maximum surface temperature contains a large amount of geographic information, which reflects the impact of human activities on the geographic environment and provides theoretical support for decisions related to transforming the geographic environment.