Buildings use large amounts of energy for heating and cooling. Cutting carbon emissions requires accurate forecasts of that demand, which depends on knowing the outdoor temperature at which buildings switch on heating or cooling. We combined energy use records with climate information and applied machine learning to create the first global dataset of these thresholds across many regions. It improves energy demand predictions by about 10 %, supporting smarter energy planning and climate policy.