Temperature extremes, heatwaves, and cold snaps pose significant risks to urban infrastructure, public health, economic productivity, and energy systems, making it essential for cities to understand the drivers of these events for effective planning and preparedness. This study quantifies changes in temperature extremes across the Continental United States (CONUS) and examines the relative roles of various climate forcing factors, including greenhouse gas concentrations, land-use change, and natural variability, with particular attention to implications for growing urban areas. Daily maximum and minimum near-surface air temperatures from the Community Earth System Model were analyzed under three forcing scenarios: natural-only, greenhouse-gas-only, and all-forcings combined. Heat waves were defined as six or more consecutive days exceeding the 99th percentile of daily maximum temperature; cold snaps were analogously identified using the 1st percentile of daily minimum temperature. Trend analysis was applied at both CONUS and gridcell scales, and an optimal fingerprinting regression framework was employed for formal detection and attribution. Preliminary results indicate that under all-forcings, extreme hot days increased significantly (slope: 105.45 days/year, p < 0.001) and heat wave frequency rose by 5.02 waves/year (p < 0.001), while cold days exhibited a non-significant declining trend. The attribution scaling factor of 3.51 suggests that combined forcings amplify heat wave occurrence roughly 3.5-fold relative to natural variability alone. Spatially, the Southwest, West, and Florida showed the strongest intensification, overlapping with regions of rapid population growth; gridcell analysis of the 15 most populated cities confirmed increasing heat wave trends in most urban areas. Building on these single-hazard, single-model results, the next phase of this work will extend the framework to CMIP5 and CMIP6 multi-model ensembles and to compound drought–heatwave events, using a daily-scale bivariate SPI–SHI approach, in order to more robustly attribute changes in compound extremes and better characterize urban vulnerability in a warming climate.