Neglecting humidity may lead to overestimated economic impacts of cooling demand

Cooling demand projections are essential for climate adaptation and energy policy, yet many studies rely on cooling degree days (CDD) based on daily mean temperature alone, ignoring humidity and diurnal temperature variation. Human thermal comfort and building cooling needs depend strongly on moisture and peak temperatures; neglecting these factors may bias global estimates of future cooling energy use and its economic impacts. Here, we compare four CDD calculation methods that differ in whether they use dry-bulb or wet-bulb temperature and in whether they use daily mean values or daily maximum and minimum temperatures. We feed these CDD estimates into an integrated assessment model (AIM/CGE) to project cooling energy consumption and economy-wide impacts to 2100 under multiple climate scenarios. We find that methods incorporating humidity and daily temperature extremes consistently yield lower CDD growth, lower projected cooling energy use, and smaller economic impacts than the conventional temperature-only approach. Under high-forcing scenarios, comprehensive methods project cooling demand and GDP losses from cooling-related expenditure approximately 25~\% to 65~\% lower than standard estimates. These results suggest that neglecting humidity in cooling demand indicators may lead to overestimated economic impacts of climate change, and that updated metrics better aligned with thermal comfort can support more calibrated policy and planning.