The south shore of Oʻahu, Hawaiʻi is one of the most visited coastal tourism areas in the United States with some of the highest instances of recreational waterborne disease. A population of the pathogenic bacterium Vibrio vulnificus lives in the estuarine Ala Wai Canal in Honolulu which surrounds the heavily populated tourism center of Waikīkī. We developed a statistical model to predict V. vulnificus dynamics in this system using environmental measurements from moored oceanographic and atmospheric sensors in real time. During a year-long investigation, we analyzed water from 9 sampling events at 3 depths and 8 sites along the canal (n = 213) for 36 biogeochemical variables and V. vulnificus concentration using quantitative polymerase chain reaction (qPCR) of the hemolysin A gene (vvhA). The best multiple linear regression model of V. vulnificus concentration, explaining 80% of variation, included only six predictors: 5-day average rainfall preceding water sampling, daily maximum air temperature, water temperature, nitrate plus nitrite, and two metrics of humic dissolved organic matter (DOM). We show how real-time predictions of V. vulnificus concentration can be made using these models applied to the time series of water quality measurements from the Pacific Islands Ocean Observing System (PacIOOS) as well as the PacIOOS plume model based on the Waikīkī Regional Ocean Modeling System (ROMS) products. These applications highlight the importance of including DOM variables in predictive modeling of V. vulnificus and the influence of rain events in elevating nearshore concentrations of V. vulnificus. Long-term climate model projections of locally downscaled monthly rainfall and air temperature were used to predict an overall increase in V. vulnificus concentration of approximately 2- to 3-fold by 2100. Improving these predictive models of microbial populations is critical for management of waterborne pathogen risk exposure, particularly in the wake of a changing global climate