Heatwaves are a leading contributor of weather-related mortality, globally contributing to thousands of deaths each year. The impacts on humans may be direct or indirect through avenues such as heat stress, strained medical capacity, infrastructure breakdown, and reduced crop yields. While extreme heat is often measured by temperature and humidity, Wet Bulb Globe Temperature (WBGT) is commonly used to evaluate real-time heat stress risks in humans and correlates better with heat related illness, and is used by the Occupational Safety and Health Administration (OSHA) and the US Army. WBGT is a weighted average of air temperature, natural wet bulb temperature, and black globe temperature. A local hourly, daily, and monthly WBGT climatology will allow those planning outdoor work to minimize the likelihood of heat related disruptions. Further, understanding the characteristics of heat waves will allow emergency planners and responders to know what to expect when heat waves occur. Additionally, evaluating the predictability of WBGT heat waves allows an understanding of what advance warning may be possible and when confidence may be higher. In this study, WBGT is calculated from the ERA5 reanalysis and is validated by the Oklahoma Mesonet and found to be adequate. Two common methods of calculating WBGT from meteorological observations are compared. The Liljegren method has a larger diurnal cycle than the Dimiceli method, with peak WBGT about 1 °F higher. The high and extreme risk categories in the southern United States Great Plains (USGP) have increased from 5 days per year to 15 days from 1960-2020. Additionally, the largest increases in WBGT are occurring during DJF, potentially lengthening the warm season in the future. Heat wave definitions based on maximum, minimum, and mean WBGT are used to calculate heat wave characteristics and trends with the largest number of heat waves occurring in the southern USGP. Further, the number of heat waves is generally increasing across the domain. This study shows that heat wave days based on minimum WBGT have increased significantly which could have important impacts on human heat stress recovery. The predictive skill of WBGT heat waves is evaluated using ERA5 reanalysis and models from the S2S Project Database. North American atmospheric regimes are defined using K-Means clustering of detrended standardized 500 mb geopotential anomalies from ERA5 reanalysis. Additionally, heat wave types (e.g. Hot-dry or warm-humid) are defined using the standardized anomalies of temperature and humidity relative to other heat waves during the same season. An analysis of the predictive skill of atmospheric regimes, heat wave types, seasonality, and the impact of ENSO and the MJO is conducted using these datasets both with zero days lead time and at S2S lead times. Finally, regime statistics are calculated in S2S model forecasts to identify skill and forecast biases. Each regime has unique heat wave frequency, type, and seasonality characteristics. Heat waves may occur in the US Great Plains with greater than twice the climatological frequency in some regimes, however the increase is often seasonally dependent. Additionally, some skill is shown at discriminating between heat wave types in different regimes. Further, the regimes that are conducive to heat waves at short lead times differ from those that correlate with heat wave occurrence at longer lead times. When incorporating the ENSO or MJO phase heat wave in addition to the atmospheric regime, some additional predictive skill is observed, with the MJO providing more skill, as it introduces larger variation between regimes and provides information regarding timing of individual heat waves where the ENSO phase does not due to the much longer period. Combining both the ENSO and MJO phase provides the highest skill, with some ENSO/MJO/regime combinations having historically observed heat wave rates in excess of 50% at some lead times while other combinations are near 0% historically, thus leading to forecasts of opportunity when a signal for higher heat wave rates occurs. S2S models show statistically significant skill in forecasting most regimes up to 5 weeks lead time. However, the accuracy at predicting the correct atmospheric regime may not be useful beyond 1-2 weeks, which may limit the ability to incorporate regimes into heat wave forecasts at longer lead times, as this information is necessary for combination statistics that incorporate regimes.