Presented at the 2018 AGU Fall MeetingWhen water evaporates from the oceans, it causes an increase in the Sea Surface Salinity (SSS). A significant portion of this evaporated water precipitates onto land. This provides a solid connection between change in Sea Surface Salinity and precipitation on land. Currently, it is more typical to use Sea Surface Temperature (SST) as a predictor for land precipitation. SSS is rarely included as a predictor because anomalies in SSS and in SST are often highly correlated and caused partially by the same phenomena, resulting in multicollinearity in the model. Here, we attempt to build features for a Linear Regression Model to fit the observed precipitation across the continental US using SSS and SST at lags on the scale of several months to evaluate and understand their relative merits and contributions to predictive skill. We use Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to find regions in the ocean that, when aggregated over for SSS or SST, can be used as predictors for precipitation in the US. In addition, we propose a way to combine these SSS and SST predictors, accounting for the cross correlation between them. We also provide additional analysis on the locations and persistence of these predictors, which ensures the predictive relationships are physically meaningful and based on a dynamical understanding of modes of variability, such as the El Niño-Southern Oscillation, and their respective teleconnection patterns. We create separate models for each month of prediction for nine sub-regions in the US, which were segregated using the k-means method to highlight regions with common precipitation characteristics. Our focus is on building a generalized and objective process of building features that can be applied to model the precipitation in any month or any region, and we strive to avoid using model-specific methods wherever possible. This paper details the pipeline of building these models, and the reasons behind each step of the pipeline. Case studies highlight the method's utility and implications for the prediction of extreme precipitation events will be discussed as well.