Global warming and societal development have inevitably increased residential and commercial building energy consumption, which accounts for 40% of total energy consumption globally. Projections of building energy consumption driven by scenarios that represent moderate and severe shifts in what climate may be like in the future paired with socioeconomic pathways can inform future energy system planning. Heating and cooling degree-days and population distribution are widely used as indicators to estimate large-scale building energy consumption. In this study, we develop an open-source R package, helios, to calculate the impact of climate change and population on 21^st century projections of heating and cooling degree-hours for the Global Change Analysis Model – USA (GCAM-USA). GCAM-USA has greater spatial and temporal details in the USA to explore the role of buildings in energy system transitions associated with evolving land, water, and socioeconomic systems. Helios transforms projected distributions of hourly temperature and population at 12-km resolution over CONUS to segmental and population-weighted heating and cooling degree-hours for each month (e.g., Jan-day and Jan-night) for each U.S. state. For the rest of the world, heating and cooling degree-hours are estimated at a monthly time step and GCAM regional level. Helios enhances GCAM’s capability to capture the impacts of spatial and temporal climatic and socioeconomic variations on building energy consumption. More broadly, helios is designed to be used by scientists who study multisectoral systems dynamics in the context of global change, including socioeconomics, technology, climate, and policy.