This study sought to develop models for predicting near-term (1–3 day) fecal contamination events in coastal shellfish growing waters. Using Random Forest regression, we (1) developed fecal coliform (FC) concentration models for shellfish growing areas using watershed characteristics and antecedent hydrologic and meteorologic observations as predictors, (2) tested the change in model performance associated when forecasted, as opposed to measured, rainfall variables were used as predictors, and (3) evaluated model predictor importance in relation to shellfish sanitation management criteria. Models were trained to 10 years of coastal FC measurements (n = 1285) for 5 major shellfish management areas along the Florida (USA) coast. Model performance varied between the 5 management areas with R2 ranging from 0.36 to 0.72. Antecedent precipitation variables were among the most important predictors in the day-of forecast models in all management areas. When forecasted rainfall was included in the models, wind components became increasingly important. •1–3 day fecal coliform forecast models were trained with regulatory shellfish data.•5 variations of Random Forest models for 5 management areas were evaluated.•Predictive performance ranged from R2 of 0.36 to 0.72.•Using forecasted rainfall as a predictor did not improve model performance.•Variable importance differed between models, implying varied prediction mechanisms.