Robust predictions of stream solute concentrations expected under natural (reference) conditions would help establish more realistic water quality standards and improve stream ecological assessments. Models predicting solute concentrations from environmental factors would also help identify the relative importance of different factors that influence water chemistry. Although data are available describing the major factors controlling water chemistry (i.e., geology, climate, atmospheric deposition, soils, vegetation, topography), geologic maps do not adequately convey how rocks vary in their chemical and physical properties. We addressed this issue by associating rock chemical and physical properties with geological map units to produce continuous maps of percentages of CaO, MgO, S, uniaxial compressive strength, and hydraulic conductivity for western United States lithologies. We used catchment summaries of these geologic properties and other environmental factors to develop multiple linear regression (LR) and random forest (RF) models to predict base flow electrical conductivity (EC), acid neutralization capacity (ANC), Ca, Mg, and SO4. Models were derived from observations at 1414 reference-quality streams. RF models were superior to LR models, explaining 71% of the variance in EC, 61% in ANC, 92% in Ca, 58% in Mg, and 74% in SO4 when assessed with independent observations. The root-mean-square error for predictions on validation sites were all <11% of the range of observed values. The relative importance of different environmental factors in predicting stream chemistry varied among models, but on average rock chemistry > temperature > precipitation > soil = atmospheric deposition > vegetation > amount of rock/water contact > topography.