Projected Spatial Asymmetry of Daytime and Nighttime Temperature Extremes in the Middle East under CMIP6 Warming

The Middle East is one of the world’s most prominent climate-change hotspots, where future warming is expected to intensify both daytime and nighttime temperature extremes in spatially heterogeneous patterns. This study investigates projected asymmetry between daytime and nighttime thermal extremes across the Middle East using four temperature-extreme indices from the Expert Team on Climate Change Detection and Indices (ETCCDI): TX90p, TN90p, TXx, and TNx, derived from Stacking-EML-refined CMIP6 projections based on six global climate models for three future periods under SSP1-2.6, SSP2-4.5, and SSP5-8.5. To move beyond regional mean change, the analysis combines delta-based projections relative to the 1985–2014 baseline with Global Moran’s I, Getis–Ord Gi* hotspot statistics, and two day–night asymmetry metrics. Results show substantial intensification of both daytime and nighttime extremes. During late-century summer under SSP5-8.5, the percentile-based indices indicate widespread exceedance of historical warm thresholds, while the intensity-based indices show substantial increases in absolute extreme-temperature magnitude. Regional mean changes during 2075–2100 reach 73.93 and 65.87 percentage points for TX90p and TN90p, respectively, while TXx and TNx increase by 11.22 °C and 7.06 °C relative to the 1985–2014 baseline. Projected changes are also highly spatially organized, with the Arabian Peninsula consistently emerging as the principal hotspot of amplification. Despite the strengthening of both daytime and nighttime extremes, asymmetry metrics indicate that future change is generally daytime-dominated at the regional scale, although localized nighttime-dominated responses persist in specific subregions. These findings show that future extreme heat in the Middle East is likely to evolve through spatially heterogeneous changes in frequency, intensity, and day–night expression, with important implications for regional climate-risk assessment.