Building and urban simulation under future climate: A novel statistical downscaling method for future hourly weather data generation

Climate change presents a major threat to the built environment and therefore requires reliable future climate data for building performance simulation (BPS). The implementation of advanced statistical downscaling methods remains difficult in BPS studies because specific historical weather data and complex implementation procedures are usually requested. The current statistical downscaling methods that are frequently used in BPS analysis were rarely validated against measurements to see if ongoing climate change process and weather extremes can be represented. This paper presents a new Distribution Adjusted Temporal Mapping (DATM) technique for downscaling future hourly weather data from the monthly GCM (Global Climate Model) data with Typical Meteorological Year (TMY) data being the baseline. The proposed method involves fitting probability distributions to TMY data for each climate variable, modifying these distributions according to the projected monthly changes from GCMs, and then mapping the future hourly weather data from the adjusted distributions. DATM is compared with the “morphing” technique for various climate variables and locations, and is validated against ten years onsite measured hourly weather data from 2015 to 2024. The outcomes reveal that DATM outperforms the morphing method in temperature downscaling in terms of reproducing climate variabilities and extreme events. For relative humidity and wind speed, DATM is slightly better in capturing the full range of variables even though both methods have their limitations. For solar radiation, DATM can reflect realistic peak solar radiation prediction in future climate downscaling. It also shows better performance in capturing the changes in temperature variability and extremes that are essential for the overall building resilience analysis. The results of both methods depend on climate zones and variables, which underlines the necessity of considering regional factors in climate data preprocessing. With climate change affecting the built environment, the proposed method in this research offers BPS researchers a more reliable way of evaluating future building performance under future emission scenarios.