Meteorological time series data include events with varying intensity that global warming due to climate change can have a different effect in any part of data distribution. Changes may not be uniform in various parts of the data distribution. Also, there may be an increasing or decreasing trend visible in some parts of the series distribution, generally, the whole series will not change. Accordingly, the use of linear regression method or nonparametric tests to detect the trend that only focuses on the mean or median of the data series, may not lead to the correct results. Therefore, quantile regression method is used to analyze trend in quantiles of data series. Gorgan is one of the important agricultural poles in Iran, where any temperature variation can affect the crops and horticultural plants of that area. For this purpose, in this research, minimum and maximum daily temperature in 34 years’ period (1985-2018) from Gorgan’s Hashemabad synoptic station was used to determine any variations in data series trend using quantile and linear regression method in seasonal time scale. The results showed that all seasons are moving towards warmer weather conditions, and this increase in temperature is much higher in summer season, but lower in spring season. Also, in upper extreme values, hot days, have a steeper trend. According to the results of quantile regression, it can be concluded that temperature’s climate condition is changing in Gorgan and this region is moving towards warmer weather condition.