Given the 24-h turn-around time before swimming advisories are released, advisories issued to protect public health really only indicates ‘it may be unsafe to swim yesterday’. Predictive modelling for Escherichia coli concentrations at inflow-impacted beaches may be a favourable alternative to the current, routinely criticised monitoring approach. Using a total of 482 sets of meteorological and bacteriological data covering 14 swimming seasons, as well as environmental data of 10 inflow streams, this study developed models that could be used for predicting E. coli concentrations at five Lake Rotorua beaches. The models include predictor variables such as wind speed, antecedent rainfall, suspended solids at Puarenga, Utuhina and Ngongotaha stream inflows and particulate inorganic phosphorus concentration at Puarenga stream inflow. The combined 2011–2012 models had an average-adjusted R2 of 0.73, root mean square error (RMSE) of 0.33 logCFU/100 mL and captured 38 % of the variance in the validation data when used to predict E. coli concentrations for an additional 2 years (2013–2014). Among the individual beach models, predictive accuracy ranged from 88.89 to 92.31 % for the three beaches considered in the study. The developed models can provide a faster estimation of E. coli condition, potentially assisting local beach managers in the decision process related to swimming advisories issuance.