High temperature and heat waves (HT and HW) directly affect human health and crop growth. Investigating trends in the occurrence of HT and HW is one of the fundamental issues of research on climate change and can provide valuable information for life and production. Most of the previous studies on trends in the occurrence of HT and HW used ordinary least squares (OLS) method to calculate the magnitude of the linear trend and then used the Student’s t-test to determine the statistical significance of this trend. This study examined whether traditional methods are suitable for estimating the trend of HT and HW occurrence in China. By showing a case of the annual HT count, with extremely excessive occurrences in 2018 at a station in northeastern China, the authors illustrate that the OLS method is sensitive to outliers and can give a spurious trend. In addition, through normality tests and autocorrelation calculations, we found at least 91.14% of stations and 90.06% of grid boxes for the annual HT count and 92.18% of stations and 87.74% of grid boxes for the annual count of HW in China are non-Gaussian, and majority of them have serial correlation. Applying a nonparametric method that is insensitive to outliers and takes serial correlation into account, we provide a more accurate estimate of linear trends in the annual HT and HW count for each station and grid box, four typical regional averages, and China area-average for the 1960-2018 period. The results show that stations with statistically significant upward trend in HT occurred mainly in South China and northwestern China, and HW stations occurred almost only in South China and in several stations in the Xinjiang Autonomous Region. In terms of the area-averaged time series of the annual HT and HW count, only South China and northwestern China show a statistically significant upward trend, while North China and northeastern China did not exhibit a significant upward trend; those of the average in China are statistically significant. This study provides reference information for choosing the method for estimating trends and their statistical significance and for statistical predicting for the occurrence of HT and HW.