Traditional detection and attribution methods rely on Pearl causal inference techniques to identify the anthropogenic influence on climatic variables. However, as such analyses depend on multi-decadal simulations of relatively coarsely resolved global climate models, confidence is limited in attribution statements about precipitation due to their often poor simulation of this aspect of the climate system. Here we present a data driven approach, using in situ measurements of daily CONUS precipitation and multi-covariate statistical models, to isolate the contrasting effects of anthropogenic greenhouse gas and sulfate aerosol emissions. While the effect of greenhouse gas emissions is to increase mean and extreme precipitation in all seasons, sulfate aerosol emissions tend to inhibit these increases in the winter and spring but enhance them in the summer and fall. Inconsistencies with climate model simulations are discussed, highlighting their inability to reproduce relevant storm characteristics.