Data quality and climate forecasting performance deteriorates because of long climate data contaminated by non-climatic factors such as the station relocation or new instrument replacement. For a trusted climate forecast, it is necessary to implement data quality control and test inhomogeneous data. Before the inhomogeneity test, a reference series was created by $d$ index to measure the temperature series relationship between the candidate and surrounding stations. In this study, a inhomogeneity test to each season and climatological station was performed on the daily mean temperatures, daily minimum temperatures and daily maximum temperatures. After comparing two inhomogeneity tests, the traditional and the adjusted SNHT method, we found the adjusted SNHT method was slightly superior to the traditional one.