This study investigates the human influence on precipitation changes over the contiguous United States (CONUS) by decomposing the combined anthropogenic signal into the effects of well-mixed greenhouse gases (GHGs) and aerosols. The authors use a regional detection and attribution (D&A) framework that explicitly models natural drivers of precipitation and accounts for both anthropogenic aerosols and GHG emissions. They find that GHG emissions increase mean and extreme precipitation across all seasons, while the decadal-scale effect of global aerosol emissions decreases precipitation. Local aerosol emissions offset GHG increases in winter and spring but enhance rainfall in summer and fall. The conflicting literature on historical precipitation trends can be explained by the offsetting signals of aerosols and GHGs. The study also assesses the performance of global climate models (GCMs) in attributing human-induced changes to precipitation, finding that while many models capture the expected GHG-driven increases, there is significant uncertainty and model disagreement. The results highlight the importance of considering century-length records and multiple anthropogenic forcing agents in regional D&A analyses, and provide insights into the complex interactions between GHGs and aerosols in shaping precipitation patterns.This study investigates the human influence on precipitation changes over the contiguous United States (CONUS) by decomposing the combined anthropogenic signal into the effects of well-mixed greenhouse gases (GHGs) and aerosols. The authors use a regional detection and attribution (D&A) framework that explicitly models natural drivers of precipitation and accounts for both anthropogenic aerosols and GHG emissions. They find that GHG emissions increase mean and extreme precipitation across all seasons, while the decadal-scale effect of global aerosol emissions decreases precipitation. Local aerosol emissions offset GHG increases in winter and spring but enhance rainfall in summer and fall. The conflicting literature on historical precipitation trends can be explained by the offsetting signals of aerosols and GHGs. The study also assesses the performance of global climate models (GCMs) in attributing human-induced changes to precipitation, finding that while many models capture the expected GHG-driven increases, there is significant uncertainty and model disagreement. The results highlight the importance of considering century-length records and multiple anthropogenic forcing agents in regional D&A analyses, and provide insights into the complex interactions between GHGs and aerosols in shaping precipitation patterns.