Forestry is pervasive across temperate North America and may influence aquatic environmental conditions such as flows and temperatures, as well as important species such as Pacific salmon (Oncorhynchus spp.). While there have been many large-scale forestry experiments using paired catchment designs, these studies have yet to be quantitatively synthesized. Thus, it remains unclear whether forestry impacts are consistent, context-dependent, or unpredictable. This study aims to quantitatively synthesize forestry impacts on streamflow and temperature, through a systematic review and synthesis of paired catchment studies across the range of Pacific salmon. Specifically, we investigated whether generalizable relationships exist between forestry intensity (percent watershed harvested) and impacts to streamflow and temperature. We also examined whether watershed features (climate, hydrology, lithology) and harvest method mediated forestry impacts. We extracted information from 35 unique paired-catchments from California to Alaska. Forestry had strong impacts on peak and low flows and maximum summer water temperatures, but responses were quite variable. Across all catchments, forestry elevated peak flows ~20% (n = 31 catchments), reduced low flows ~25% (n = 13 catchments), and increased maximum summer temperatures ~15% (n = 35 catchments) on average. However, these impacts were variable and were not predictable based on forestry intensity, thus broader stressor-response relationships were not supported. Forestry impacts on peak flows and maximum summer temperatures varied spatially. Peak flow impacts increased with increased northward latitude and temperature impacts decreased with eastward longitude. However, the magnitude of impacts were unrelated to other watershed attributes, which included climate (precipitation and aridity), rain vs. snow hydrology, elevation, and bedrock lithology. Harvest method and riparian buffer presence also had no detected effects on forestry impacts across studies and statistical models explained a low proportion of variation overall. Collectively, our results indicate that forestry can have substantial impacts on key environmental conditions; however, the magnitude of impact was variable and could not be clearly linked to easily-measured watershed characteristics. This implies that forestry impacts are not broadly predictable. Probabilistic risk models based on distributions of potential impacts may therefore be more useful for watershed management in data-poor situations.