This study seeks to analyze the variability and determine the significance level of daily temperatures trends during 1985-2018 using four statistical tests, which are the Semi-averages, Moving averages, T-student and Mann-Kendall. This study relied on daily dataset of three temperatures indices collected from NCM (National Center of Meteorology) for 34 years (1985-2018). The methodology of this study deals with the analysis of the statistic distribution of dataset of the selected (Tdmax), Minimum daily (Tdmin) and Daily average (Tdmean) during the period 1985-2018 by applying the Shapiro-Wilk test. The statistical significance of the Trends variance of extreme daily temperatures was analyzed using four statistical tests, which are the Semi averages, Moving averages, T-student and Mann-Kendall. The results of the statistical analysis were also represented on thematic maps. This study revealed the spatial variations of the daily temperatures and their trends to infer the level significance in analyzing the climate change among the selected regions of Saudi Arabia. The funding show many effective differences in spatial distribution of the daily temperatures in the selected meteorology stations. In the context of Trends analysis, the T-student test revealed the clearly differences between the semi-averages during the two periods of 1985-2001 and 2002-2018. The mean of daily temperature had an increased significantly trend in all stations, except Turayf with a rate ranged from 0.1oC/decade in Al Hassa and 0.62oC/decade in Abha at a significance level of 0.001. The mean of the minimum daily temperature had an increased significantly trend in all stations at a significance level of 0.001. The mean of the maximum daily temperature had also an increased significantly trend in all stations, at a significance level ranged from 0.001 to 0.05, except Qurayate and Turayf. This study was able to represent the variation of the spatial distribution of daily temperatures using the statistical tests for determining the significance of the trends during the period 1985-2018. The integrated employment of the methods can give more accurate results in inferring climate change indicators over Saudi Arabia regions.