Heat stress events in dairy cows are associated with behavioral and physiological changes such as seeking shade, increased respiration rate and body temperature, reduced milk production, and psychological distress. Knowledge of the relationship between weather and animal responses to heat stress enables automated alerts using forecast weather, aiding early provision of shade or other mitigation practices. While numerous heat stress indices for cattle have been developed, these have limitations for cows exposed to wind and solar radiation (i.e., predominantly grazing outdoors or managed on pasture). To develop a predictive model for heat stress events in pasture-based dairy systems, rumen temperature data from smaXtec (smaXtec animal care GmbH, Graz, Austria) rumen boluses in 443 cows on 3 dairy farms in Northland, New Zealand, were used to identify heat stress events and these were matched with automated weather station data collected on or near the farm. Heat stress rate (HSR) was defined as the percentage of cows within an age-breed group having a rumen temperature greater than 3 standard deviations above an individual cow's mean and heat stress events were defined as HSR >25%. Single and multiple linear regression models, including published heat stress indices, were generally able to predict a high proportion of heat stress events (sensitivity 34%-68%), but were insufficiently discriminating, predicting also a high number of false positives (precision only 9%-27%). A machine learning algorithm, cubist, was the best performing model, predicting 79% of heat stress events with a precision of 52% for this dataset. Our proof-of-concept study demonstrates the potential of this approach, using climate data to predict and forecast heat stress events in pasture-based dairy systems. Further work should test the cubist model using independent data, refine dataset construction, investigate the value of including known animal variables such as cow age or breed, and incorporate other measures of heat stress such as respiration rate.