<p>Climate change poses a severe threat to Pakistan and the wider South Asian region, where shifting temperature and precipitation patterns increasingly jeopardise agricultural productivity, water security, and socio-economic stability. This study examines the spatially resolved, seasonally varying drivers of temperature across 161 Pakistani districts (1981–2023) by incorporating surface pressure, wind speed, precipitation, relative humidity, and aerosol optical depth into a Geographically and Temporally Weighted Regression (GTWR) framework, treating temperature as the dependent variable. Bandwidth and kernel parameters were optimised via a corrected Akaike Information Criterion grid search. Benchmarked against global (OLS), spatial-only (GWR), and temporal-only (TWR) regressions, GTWR achieves the lowest AICc and residual sum of squares (2.04 vs. 62.5–123.3), and reduces mean residual Moran's I to 0.016 from 0.35–0.40, confirming that jointly modelling spatial and temporal non-stationarity substantially improves fit and residual independence rather than yielding only marginal gains in variance explained (R² = 0.9997). Spatial patterns show the strongest warming signals in southern and urbanised districts, with temperature extremes concentrated in the northeast and moderated in the southwest. Marked seasonality emerges across the DJF, MAM, JJA, and SON periods, with the South Asian monsoon identified as a dominant control on summer precipitation across Pakistan's heterogeneous topography. This analysis quantifies region- and season-specific climatic dynamics that remain sparsely documented in the Pakistani context, offering geospatially resolved, policy-relevant evidence for adaptation planning, including water resource management, agricultural resilience, and national climate-action policy, with a transferable methodological template for other topographically complex, monsoon-influenced regions.</p>