High temperature and heatwave (HT and HW) directly impact human health and crop growth. Investigating the trends in the occurrence of HT and HW is one of the fundamental questions of climate change research and can provide valuable information for living 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 linear trend and then used student’s t-test to determine the statistical significance of this trend. This study examined whether traditional methods are suitable for the trend estimation of the occurrence of HT and HW in China. By showing a case of the annual count of HT days with extremely excessive occurrences in 2018 at a station in northeastern China, we illustrated that OLS method is sensitive to outliers and can give spurious trend. Further, through normality testing and autocorrelation calculation, we found at least 91.14% of stations and 90.06% of grid boxes for the annual count of HT days and 92.18% of stations and 87.74% of grid boxes for the annual count of HW in China are non-Gaussian, and the majority of them have serial correlation. Applying a nonparametric method that is insensitive to outliers and takes into account serial correlation, we gave a more accurate estimation of the linear trends in the annual count of HT days and HW for every station and grid box, four typical regions average, and China area-average for the period 1960~2018. The results show that stations with statistically significant increasing trend in HT days occurred mainly in South China and northwestern China, and those in HW occurred nearly only in South China and several stations in Xinjiang Autonomous Region. In terms of area average of the trend in annual count of HT days and HW, only South China region and northwestern China region show statistically significant increasing trend, whereas North China and northeastern China not significant; those of China average are both significant. This study provides referential information for the choice of method in the estimation of trend and its statistical significance and in statistical prediction for HT days and HW.