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Skillful subseasonal prediction of extreme heat and precipitation greatly benefits multiple sectors, including water management, public health, and agriculture, in mitigating the impact of extreme events. A statistical model is developed to predict the weekly frequency of extreme warm days and 14-day standardized precipitation index (SPI) during boreal summer in the United States. We use a leading principal component of U.S. soil moisture and an index based on the North Pacific sea surface temperature (SST) as predictors. The model outperforms the NCEP Climate Forecast System, version 2 (CFSv2), at weeks 3–4 in the eastern United States. It is found that the North Pacific SST anomalies persist for several weeks and are associated with a persistent wave train pattern, which leads to increased occurrences of blocking and extreme temperature over the eastern United States. Extreme dry soil moisture conditions persist into week 4 and are associated with an increase in sensible heat flux and a decrease in latent heat flux, which may help to maintain the overlying anticyclone. The clear-sky conditions associated with blocking anticyclones further decrease soil moisture and increase the frequency of extreme warm days. This skillful statistical model has the potential to aid in irrigation scheduling, crop planning, and reservoir operation and to provide mitigation of impacts from extreme heat events. [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: 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. Key Points: 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 [ABSTRACT FROM AUTHOR]