As oceanic moisture evaporates, it leaves a sea surface salinity signature. Roughly 10% of the moisture that evaporates over the ocean is transported over land, allowing the salinity fields to be a predictor of terrestrial precipitation. The research I will present is among the first in the published literature to assess the role of sea surface salinity for predictions on low-skill summertime subseasonal timescales for terrestrial precipitation predictions. Neural networks are trained with the CESM2 Large Ensemble using North Atlantic salinity anomalies to quantify predictability of U.S. Midwest summertime heavy rainfall events at 0 to 56-day leads. Using explainable artificial intelligence, salinity anomalies in the Caribbean Sea and Gulf of Mexico provide skill for subseasonal forecasts of opportunity, e.g., confident and correct predictions. Further, a moisture-tracking algorithm applied to reanalysis data demonstrates that the evaporation-dominant regions identified by neural networks directly provide moisture that precipitates in the Midwest. The role of the vertical resolution of the sea surface salinity data on subseasonal predictability is additionally explored, revealing sensitivities of the prediction skill in numerous CMIP6 models.