A novel approach of hourly weather data downscaling from GCM for building performance simulation

Climate change presents a major threat to the built environment, and therefore requires reliable future climate data for building performance simulation (BPS). The current approaches to downscaling future weather conditions are rarely developed for BPS studies and have challenges in representing climate change and its range, especially in the case of extreme conditions.

This paper presents a new Distribution Adjusted Temporal Mapping (DATM) technique for scaling down the future hourly weather data from the monthly GCM 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 onsite measured hourly weather data of San Francisco from 2015-2023.

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 the variables even though both methods have their limitations. DATM also shows better performance in capturing the changes in temperature variability and extremes that are essential for the overall building resilience analysis.