Studying the trend of Temporal and Spatial Changes in Extreme Quantiles of Minimum and Maximum Temperature in Iran

Climate change is a condition that refers to any change in climate that occurs over time. These changes may not be seen in the average of the data series but occur in quantiles of the series with different intensities that can be examined by quantile regression. In this research, to investigate Spatio-temporal changes of maximum and minimum temperature in different seasons in Iran, after fitting quantile regression on 102 meteorological stations with the statistical period of 2016-1986, the slope of the trend was calculated in the different quantiles, and by comparing its results with the results of the ordinary linear regression, they were spatially zoned. The results showed that in the spring season, the highest slope of the increasing trend is in the lower quantiles and the eastern half of Iran, but in the winter season, it was in the middle and upper quantiles and the northwest and west of Iran. In the summer season, the lower quantiles had an increasing trend, but in the autumn season, they had a decreasing trend. However, in spring and summer, the lower quantiles of daily temperature and in autumn and winter, the upper quantiles increased more strongly. Also, the comparison of the two regression methods shows that most of the quantiles had a slope different from the slope of the least square regression, and it’s not correct to assign the slope of the ordinary square regression line for the trend of temperature changes in the whole series. Finally, it can be stated that climate change has occurred in the maximum and minimum daily temperatures in Iran, but the intensity of these changes varies according to different seasons, quantiles, and locations.