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Tornadoes are one of the high-impact weather phenomena that can induce life loss and property damage. Here, we investigate the relationship between large-scale weather regimes and tornado occurrence in boreal spring. Results show that weather regimes strongly modulate the probability of tornado occurrence in the United States due to changes in shear and convective available potential energy and that persisting weather regimes (lasting =3 days) contribute to greater than 70% of outbreak days (days with =10 tornadoes). A hybrid model based on the weather regime frequency predicted by a numerical model is developed to predict above/below normal weekly tornado activity and has skill better than climatology out to Week 3. The hybrid model can be applied to real-time forecasting and aide in mitigation of severe weather events. Severe storms that are capable of producing tornadoes, strong winds, and hail may lead to a high number of fatalities and property damage. The relationship between tornadoes and the large-scale, recurrent weather patterns over the United States is investigated. It is shown that specific weather patterns may alter tornado activity and that persisting weather patterns contribute to greater than 70% of tornado outbreak days. Finally, a hybrid model is created to predict tornado activity, and the skill of the model is better than using a long-term average out to Week 3, which is an improvement of the current forecasting state. The large-scale weather regimes strongly modulate United States tornado occurrenceA large fraction of tornado outbreak days are associated with a persisting regimeWeekly tornado activity can be predicted using forecast model-predicted weather regimes