Environmental Determinants of Temperature Trends in a Climatically Diverse Region: A Geographic Modelling Approach

ABSTRACT Understanding spatial heterogeneity in warming rates is crucial for localized climate adaptation. This study employs a novel multi‐scale framework—integrating principal component analysis (PCA) and Multiscale Geographically Weighted Regression (MGWR)—to analyse drivers of normalized temperature trends (NTT) across the Middle East and parts of Central Asia. The MGWR model significantly outperformed global and local alternatives (adjusted R 2 = 0.98) and showed strong predictive skill on independent data ( R 2 = 0.93). Results confirm near‐universal warming (NTT > 1) but reveal pronounced spatial heterogeneity in its rate. Drivers separate into: (1) dominant, non‐stationary factors (e.g., snow cover, elevation and, specific forest types) with effects that reverse sign regionally; (2) strong, consistent moderators (atmospheric humidity, water bodies and AOD) and the dominant urban‐warming gradient, and (3) weak, stable secondary drivers (e.g., specific signals of population density and shrublands) adding fine spatial nuance. A key insight is that PCA decomposes the aggregate anthropogenic signal into independent patterns, simultaneously revealing contexts of urban‐linked warming (via heat islands) and moderation (likely via aerosols). We conclude that warming drivers are not monolithic but stem from complex, competing local processes. This necessitates a shift from uniform strategies to spatially‐explicit, driver‐specific interventions. Our framework provides a foundation for prioritizing actions in climate‐vulnerable regions.