Land surface temperature (LST) plays a critical role in the water cycle and energy balance at global and regional scales. Large-scale LST estimates can be obtained from satellite observations; however, there are still uncertainties and challenges in the applicability of LST retrieval based on satellite data. In this study, to assess the accuracy and applicability of LST products from the Advanced Geostationary Orbit Radiation Imager (AGRI) based on the Fengyun-4A (FY-4A) satellite in the Guangxi region, the FY-4A LST products are compared with the Moderate Resolution Imaging Spectrometer (MODIS) LST products, the surface temperature (0 cm) observed by 91 meteorological stations, and the surface temperature measured by a thermal infrared radiometer. By calculating statistical indicators such as bias, root mean square error, and correlation coefficient, the difference between two sets of remote sensing LST products and their potential relationship with surface conditions are comprehensively analysed, and the consistency between FY-4A LST products and ground-observed LST is deeply evaluated. In addition, a standardised anomaly analysis method is used to evaluate the monitoring ability of FY-4A LST products and station-observed LST on high-temperature and cold-wave events. The results show that the average difference in temperature between FY-4A LST and MODIS LST is about 2 ℃, and significant differences occur in areas with high elevation and sparse tree coverage. The correlation coefficients of the time series between FY-4A LST and MODIS LST exceed 0.9 in most areas. It is also found that there is a good consistency in temporal change between the FY-4A LST products and the LST obtained by the two ground observations. However, the FY-4A LST products exhibit systematic underestimation bias compared with the ground-observed LST due to the difference in observation methods, especially in the afternoon. Based on case analyses of extreme weather events such as high temperatures and cold waves, the FY-4A LST products have a close temporal and spatial relationship with the standardised anomalies of the station-observed LST, which can monitor large areas of abnormal temperature signals with standardised anomalies >2. In general, FY-4A LST products show good monitoring capabilities and applicability in the Guangxi region, especially in supplementing the observation of surface temperature in remote mountainous areas with sparse station distributions. The results of this study demonstrate that satellite observations can comprehensively characterise the spatio-temporal variation of the LST and allow us to better use satellite-based LST products in response to changing climate in the future.