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Seasonal snowmelt is a primary contributor to annual runoff in the Upper Colorado River Basin (UCRB), a region that serves as a critical water resource for the western United States. Recent shifts in temperature and precipitation patterns due to climate change have altered the behavior of annual snowpack in the mountain headwaters, increasing variability in downstream basin hydrology. Identifying the timing and magnitude of significant hydrometeorological change is critical for assessing the impacts of anthropogenic climate change on snow-driven water resources. As many natural and human systems are adapted to local climate variability, further characterizing these changes relative to a reference climate state is essential for informing operations and management decisions. We present a multi-model framework to detect the emergence of anthropogenic climate change signals in snow water resource metrics for the UCRB. Four members of the CESM2 Large Ensemble were dynamically downscaled to a 3 km grid resolution at a daily timestep from 1980-2100. Time series for air temperature, precipitation, and peak snow water equivalent (SWE) were spatially averaged to nine HUC 6 subbasins and aggregated to seasonal and annual averages. Time of Emergence (ToE) was defined as a statistically significant deviation from historical (1980-2010) variability. On average, trends in air temperature emerge from the historical distribution for all subbasins by 2017, with a warming rate of 0.50 C / decade. Conversely, declines in annual peak snow water equivalent emerged much later, by 2072 in all but one subbasin, at an average rate of -4.08% / decade. Trends in annual or seasonal precipitation did not emerge in most subbasins. Results emphasize the importance of evaluating multiple hydrometeorological metrics in the assessment of UCRB snow water resource trends. Future work will extend to include a comprehensive evaluation of relevant metrics, including discharge and elevation bands within subbasins of the UCRB.