Effect of climate change on building cooling loads in Tokyo in the summers of the 2030s using dynamically downscaled GCM data

• We showed the series of constructing future design weather data by using dynamical downscaling.
• We confirmed the bias of obtained weather data due to both regional climate model and global climate model.
• We corrected the bias of the weather data using the statistical values of observations.
• The availability of the weather data was confirmed from the results of building energy simulations.
• The sensible heat load for a house in Tokyo was predicted to increase by 26% and latent heat load increased by 10%.

In this study, we dynamically downscaled the Model for Interdisciplinary Research on Climate version 4 (MIROC4h) in August for the present (2001–2010) and the near future (2026–2035). We selected weather data that represent the average weather conditions during 10-year periods among the results of downscaled MIROC4h. Correcting the selected weather data with observations to reduce bias of both regional climate model (RCM) and global climate model (GCM), we constructed a prototype of the near-future design weather data of the 2030s. We conducted building energy simulations using the prototype of design weather data to assess the impact of climate change on energy consumption of a two-story detached house in Tokyo. Under these conditions, total sensible heat load in August increased 26%, and the latent heat load increased 10%.