Surface temperature plays a key role in physical and biological processes on Earth, and it is an important index for evaluating surface thermal environment. Understanding the temporal and spatial variations of surface temperature is of great significance for urban heat island monitoring and ecological quality evaluation. Current land surface temperature products derived from satellite remote sensing suffer from partial coverage or cloud-cover blockage problems, which have potential limitations on the study of climate and ecological environment. The recently developed NOAA satellites High-Resolution Infrared Radiation Sounder (HIRS) land surface temperature (LST) product is among the longest LST records. To examine the spatiotemporal distribution of LST in Jiangsu Province and data performance of HIRS LST at different temporal scales, we evaluated the HIRS LST and in situ measurement correlation coefficient (R), bias, and unbiased root mean square difference (ubRMSD) using the daily, annual, and seasonal mean values, together with the long-term linear trend during 30 years in the province. Great consistency between the two products is observed. Their correlation coefficients are higher than 0.98 for all stations, and those for daily anomalies range from 0.65 to 0.80 across the region. The bias and the ubRMSD indicate that the HIRS data have generally underestimated the LST across the northern and some southern areas, mainly because of its large underestimation of the occurrences of temperature higher than 32 ℃. Nevertheless, the HIRS LST has largely overestimated the occurrences of summer days with temperature ranging from 20-30 ℃. As for the intraannual variations, the HIRS LST shows highest correlation with in situ measurements in the spring, but the correlation is lowest in the winter. Trend test shows that both sets of data show significant increasing trends with similar patterns in the spring, autumn, and winter. However, the long-term trends are significantly overestimated across the region in the summer, and underestimated in other seasons in the HIRS LST data.