Gridded time series of climatic variables are key inputs to phenological models used to generate spatially continuous indices and explore the influence of climate variability and change on plant development at broad scales. To date, there have been few efforts to evaluate how the particular source and spatial resolution (i.e., scale) of the input data might affect how phenological models and associated indices track variations and shifts at the continental scale. This study represents the first such assessment, based on cloud computing and volunteered phenological observations. It focuses on established extended spring indices (SI‐x) that estimate day of year (DOY) for first leaf (FL) emergence and first bloom (FB) emergence in plants particularly sensitive to accumulation of warmth in early to mid‐spring. We compared and validated gridded SI‐x products obtained using Daymet (at 1, 4, 35, and 100 km spatial resolution) and gridMET (at 4, 35, and 100 km) temperature data. These products were used to estimate temporal trends in DOY for FL and FB in the coterminous United States (CONUS) from 1980 to 2016. The SI‐x products, and their resulting patterns and trends across CONUS, affected more by the source of input data than their spatial resolution. SI‐x estimates DOY of FL and FB are about 3 and 4 weeks more accurate, respectively, using Daymet than gridMET. This leads to significant differences, and even contradictory, rates of change in DOY driven by Daymet versus gridMET temperatures, even though both data sources exhibit advancement in DOY of FL and FB across most regions in CONUS. SI‐x products generated from gridMET poorly estimate the timing of spring onset, whereas Daymet SI‐x products and actual volunteered observations are moderately correlated (r = 0.7). Daymet better captures temperature regimes, particularly in the western United States, and is more appropriate for generating high‐resolution SI‐x indices at continental scale.