Understanding statistical downscaling method specific difference in building simulation in future climate change

Climate change poses significant challenges to building energy performance, necessitating accurate future weather data for building simulations. This paper develops a Distribution Adjusted Temporal Mapping (DATM) technique as a new approach to climate downscaling for Building Performance Simulation (BPS). DATM combines the monthly Global Climate Model (GCM) data with historical Typical Meteorological Year (TMY) data using statistical distribution mapping for multiple climate variables. The research compares DATM with the widely used morphing method across six diverse U. S. climate zones, focusing on two prototype buildings: a single-family house and a medium office building. When comparing DATM and morphing results with actual weather data observed in recent years (2015-2024), it is found that DATM provided better results in capturing temperature variability. In projection of future climate (2030-2059), DATM forecasts more severe and more frequent heatwaves and cold snaps than the Morphing method, and thus, a higher vulnerability to possible elevated levels of extreme weather events. Both methods showed consistent trends in the forecast of increasing Cooling Degree Day (CDD) and decreasing Heating Degree Day (HDD), with DATM predicting higher CDD. BPS has disclosed important regional differences in terms of expected annual EUI, peak loads and total heating and cooling loads. The study stressed the importance of building thermal mass in moderating the effects of climate change on energy demand, which in most cases are lower than the expectation derived from the higher downscaled temperature by DATM. Although DATM normally estimated relatively smaller changes in EUI than the morphing method, especially in more variable climates, it offers a statistically adjusted representation of future extreme value distributions and magnitude variability. This research suggests that the choice of downscaling method is important in assessing climate change impacts on buildings, which can be decisive to conceive more correct, robust designs and energy solutions.