Stochastic weather generators are commonly used to overcome the lack of observational data or the computational burden of numerical weather models, they enable to simulate realistic features of weather variables in an inexpensive data-driven approach. Temperature and its extremes (cold and hot) impact the energy generation and demand, as well as infrastructures. Mathematical models are typically designed and optimized to study long-term planning of power-grid systems, in particular to improve their economic efficiency and resilience to high-impact and extremes events. Since energy demand depends on the hour and day of the year and on regional aspects, we propose to generate temperature scenarios at an hourly level over the Midwest, which serve as inputs of power-grid mathematical models. Since high-impact events in power grid are not restricted to extreme temperature, we base our model on a newly proposed probability distribution bridging the bulk and both tails of a distribution into a single comprehensive model.