Analyzing Spatiotemporal Patterns of Extreme Temperatures in Iran Using Principal Component Analysis and Quantile Regression

Abstract

This study analyzes warming patterns in Iran from 1955 to 2024 by combining Principal Component Analysis (PCA) and quantile regression to uncover temperature changes that traditional methods might miss. Initially, PCA categorized the monthly and seasonal temperatures into zones with similar regimes (PC), after which temperature variations within each PC were examined. The results show that minimum temperatures ( T _min ) are rising about 21% faster than maximum temperatures ( T _max ), indicating that warming in Iran is mainly driven by increasing T _min . The rise is pronounced in hot and arid regions. Quantile regression analysis indicates that monthly extreme cold temperatures (5th quantile of T _min ) are increasing at 0.54°C.decade ⁻¹ , which is faster than the 0.34°C.decade ⁻¹ rise in extreme hot temperatures (95th quantile of T _max ). These values for the cold and warm season are 0.72 and 0.52°C.decade ⁻¹ , respectively. This means extreme cold temperatures are increasing 59% faster on a monthly basis and 38% faster seasonally compared to extreme hot temperatures, highlighting its greater sensitivity to climate change. The temperature changes in Iran are consistent with shifts in outgoing longwave radiation (OLR) and total cloud cover (TCC). Temperature is positively correlated with OLR and negatively with TCC. Since the temperature change point in 1995, TCC has decreased by 9.5% in the cold season and 7.5% in the warm season, while OLR has increased by about 20%. These results help explain the rising temperatures in Iran.