Despite global efforts toward sustainable urban development, predictive methods for assessing outdoor thermal comfort (OTC) remain limited and computationally intensive, particularly in hot semi-arid cities such as Jaipur where increasing heat stress and inadequate thermally informed planning hinder effective climate-responsive urban design. This study develops a validated regression-based framework integrating eleven morphological parameters with reference weather variables to predict neighbourhoodscale microclimate across Local Climate Zones (LCZs) in Jaipur for both summer and winter seasons. Micrometeorological data were collected at 66 locations using a Testo 480 device, with Multiple Linear Regression (MLR) applied to analyze the combined influence of climatic and morphological parameters on air temperature, relative humidity, wind speed, and solar radiation. The summer Ta model demonstrated strong predictive performance (R2 = 0.82) and the winter model moderate fit (R2 = 0.66). Bootstrap predictor-selection analysis (B = 1000 resamples) confirmed stable selection of dominant predictors, reference air temperature, building height, and sky view factor regardless of sample composition. Leave-one-LCZ-out cross-validation (k = 11) yielded a mean predictive R2 of 0.692 and RMSE of 1.33 degrees C for summer, comparable to the 80-20 split results, confirming spatial generalisability beyond the sampled locations. Among morphological parameters, vertical area ratio outperformed floor area ratio, and impervious surface fraction exceeding 55% was associated with elevated summer air temperatures. Height variability and courtyard orientation emerged as additional thermal determinants, highlighting their potential as actionable parameters for climateresponsive design in greenfield and brownfield projects, contributing to thermal resilience in hot semi-arid cities.