Spatial interpolation is an important method for creating spatial representation of temperature in geographic and ecological research and is important for supplying fine resolution temperature data for ecological models. This paper reviews existing spatial interpolation research of meteorological factors and compares a number of interpolation methods, including global interpolators (trend surfaces and regression models), local interpolators (inverse distance weighting, gradient plus inverse distance squares method, PRISM, splines, ANUSPLIN), geostatistical methods (Ordinary Kriging, Co-kriging), and mixed methods (combined global, local, and geostatistical methods). These methods are commonly used for the spatial interpolation of temperature data. The aim of this study is to explore the suitability and inadequacies of these methods in order to provide references for future research involving spatial interpolation of temperature data. It also attempts to explore ways to improve the application of the various methods. The comparison of these methods shows that each method has its own strength in particular applications. There is no universal method suitable for all practical applications. In practice, specific geographical characteristics of the study area must be considered and tests should be done to determine the suitability of specific methods. In order to achieve optimal interpolation result of regional temperature, parameters of the methods should be adapted based on actual geographical conditions. Global interpolation and geostatistical methods can be applied to study global trends. Local interpolation based on distance similarity principle does not apply to global trends simulation. Mixed methods are able to combine advantages of global interpolation, local interpolation, and geostatistics, and improve the simulation accuracy. Mixed methods and PRISM and ANUSPLIN are more suitable for application under complex terrain conditions. In future research, integration of various temperature spatial interpolation methods will improve, and more mixed methods combining global, local, and geostatistical methods will be created. Methods based on the physical distribution characteristics of temperature and combined with GIS technology will be prevalent. In order to improve the simulation accuracy of temperature in microscopic details, introduction of additional factors, such as terrain, will be an important future trend.