Abstract. Description of thermal regimes in flowing waters is key to understanding physical 6 processes, enhancing predictive abilities, and improving bioassessments. Spatially and temporally 7 sparse datasets, especially in logistically challenging mountain environments, have limited studies 8 on thermal regimes but inexpensive sensors coupled with crowd-sourced data collection efforts 9 provide efficient means of developing large datasets for robust analyses. Here, thermal regimes are 10 assessed using annual monitoring records spanning a five-year period (2011)(2012)(2013)(2014)(2015) at 226 sites 11 across several contiguous montane river networks in the northwestern U.S. Regimes were 12 summarized with 28 metrics and principle components analysis (PCA) was used to determine those 13 metrics which best explained thermal variation on a reduced set of orthogonal axes. Four principle 14 components (PC) accounted for 93.4% of the variation in the temperature metrics, with the first PC 15 (49% of variance) associated with metrics that represented magnitude and variability and the second 16 PC (29% of variance) associated with metrics representing the length and intensity of the winter 17 season. Another variant of PCA, T-mode analysis, was applied to daily temperature values and 18 revealed two distinct phases of spatial variance-a homogeneous phase during winter when daily 19 temperatures at all sites were < 3 °C and a heterogeneous phase throughout the year's remainder 20 when variation among sites was more pronounced. Phase transitions occurred in March and 21November, and coincided with the abatement and onset of subzero air temperatures across the study 22 area. S-mode PCA was conducted on the same matrix of daily temperature values after transposition 23 and indicated that two PCs accounted for 98% of the temporal variation among sites. The first S-24 mode PC was responsible for 96.7% of that variance and correlated with air temperature variation (r 25 = 0.92) whereas the second PC accounted for 1.3% of residual variance and was correlated with 26 discharge (r = 0.84). Thermal regimes in these mountain river networks were relatively simple and 27 responded coherently to external forcing factors, so sparse monitoring arrays and small sets of 28 summary metrics may be adequate for many aspects of their description. PCA provides a 29 computationally efficient means of extracting key information elements from large temperature 30 datasets and could be applied broadly to facilitate comparisons among more diverse stream types 31 and develop classification schemes for thermal regimes. 32 33