Assessment of potential climate change impacts on stream water temperature (Ts) across large scales remains challenging for resource managers because energy exchange processes between the atmosphere and the stream environment are complex and uncertain, and few long-term datasets are available to evaluate changes over time. In this study, we demonstrate how simple monthly linear regression models based on short-term historical Ts observations and readily available interpolated air temperature (Ta) estimates can be used for rapid assessment of historical and future changes in Ts. Models were developed for 61 sites in the southeastern USA using ≥ 18 months of observations and were validated at sites with longer periods of record.The Ts models were then used to estimate temporal changes in Ts at each site using both historical estimates and future Ta projections. Results suggested that the linear regression models adequately explained the variability in Ts/sub across sites, and the relationships between Ts and Ta remained consistent over 37years. We estimated that most sites had increases in historical annual mean Ts between 1961 and 2010 (mean of +0.11°C decade-1). All 61 sites were projected to experience increases in Ts from 2011 to 2060 under the three climate projections evaluated (mean of +0.41°C decade-1). Several of the sites with the largest historical and future Ts changes were located in ecoregions home to temperature-sensitive fish species. This methodology can be used by resource managers for rapid assessment of potential climate change impacts on stream water temperature.