Climate warming will cause mountain snowpacks to melt earlier, reducing summer streamflow and threatening water supplies and ecosystems. Quantifying how sensitive streamflow timing is to climate change and where it is most sensitive remain key questions. Physically based hydrological models are often used for this purpose; however, they have embedded assumptions that translate into uncertain hydrological projections that need to be quantified and constrained to provide reliable inferences. The purpose of this study is to evaluate differences in projected end-of-century changes to streamflow timing between a new empirical model based on diel (daily) streamflow cycles and regional land surface simulations across the mountainous western USA. We develop an observational technique for detecting streamflow responses to snowmelt using diel cycles of incoming solar radiation and streamflow to detect when snowmelt occurs. We measure the date of the 20th percentile of snowmelt days (DOS₂₀) across 31 western USA watersheds affected by snow, as a proxy for the beginning of snowmelt-initiated streamflow. Historic DOS₂₀ varies from mid-January to late May among our sites, with warmer basins having earlier snowmelt-mediated streamflow. Mean annual DOS₂₀ strongly correlates with the dates of 25 % and 50 % annual streamflow volume (DOQ₂₅ and DOQ₅₀, both R²=0.85), suggesting that a 1 d earlier DOS₂₀ corresponds with a 1 d earlier DOQ₂₅ and 0.7 d earlier DOQ₅₀. Empirical projections of future DOS₂₀ based on a stepwise multiple linear regression across sites and years under the RCP8.5 scenario for the late 21st century show that DOS₂₀ will occur on average 11±4 d earlier per 1 ^∘C of warming. However, DOS₂₀ in colder watersheds (mean November–February air temperature, T_JNDF < -8 °C) is on average 70 % more sensitive to climate change than in warmer watersheds (T_NDJF > 0 °C). Moreover, empirical projections of DOQ₂₅ and DOQ₅₀ based on DOS₂₀ are about four and two times more sensitive to climate change, respectively, than those simulated by a state-of-the-art land surface model (NoahMP-WRF) under the same scenario. Given the importance of changes in streamflow timing for water resources, and the significant discrepancies found in projected streamflow sensitivity, snowmelt detection methods such as DOS₂₀ based on diel streamflow cycles may help to constrain model parameters, improve hydrological predictions, and inform process understanding.