General circulation models (GCM) have been used by researchers to assess the effect of climate change in different fields of study. In the case of building energy performance, GCMs can be used to evaluate future building energy performance through simulations. However, a key issue with the use of GCM data in building energy simulation is the inadequate resolution and bias of the data. Therefore, in order to use this data for simulation purposes and better predict future building performance, further processing is required. The first challenge is that the GCMs are usually biased, which means a considerable deviation can be found when the historical GCM data is compared to station observed weather data. The second challenge is that the GCM data has daily temporal resolution rather than the hourly resolution required in building energy simulation.

In order to utilize GCM data to estimate future building performance through simulation, the current study suggests a workflow that can be applied to climate change data. First, a bias-correction technique, known as the quantile-quantile method, is applied to remove the bias in the data in order to adapt GCMs to a specific location. The study then uses a hybrid classification-regression model to downscale the bias-corrected GCM data to generate future weather data at an hourly resolution for building energy simulation. In this case, the hybrid model is structured as a combined model, where a classification model serves as the main model together with an auxiliary regression model for cases when data is beyond the range of observed values. The proposed workflow uses observed weather data to determine similar weather patterns from historical data and use it to generate future weather data, contrary to previous studies, which use artificially generated data. However, in cases where the future GCM data showed temperatures ranging outside of the observed data, the study applied a trained regression model to generate hourly weather data.

The proposed workflow enables users to generate future weather files year by year under different climate change scenarios and, consequently, extreme weather characteristics are preserved for extreme or reliability analysis and design optimization.

研究人员已使用通用循环模型（GCM）来评估气候变化在不同研究领域中的影响。就建筑能源性能而言，GCM可用于通过模拟评估未来的建筑能源性能。但是，在建筑能耗模拟中使用GCM数据的关键问题是数据的分辨率和偏差不足。因此，为了将该数据用于仿真目的并更好地预测未来的建筑性能，需要进行进一步处理。第一个挑战是GCM通常是有偏差的，这意味着将历史GCM数据与站点观测到的气象数据进行比较时，会发现相当大的偏差。第二个挑战是GCM数据具有每日时间分辨率，而不是建筑能耗模拟中所需的小时分辨率。

为了利用GCM数据通过模拟来评估未来的建筑性能，当前的研究提出了一种可应用于气候变化数据的工作流程。首先，采用一种被称为分位数法的偏差校正技术来消除数据中的偏差，以使GCM适应特定位置。然后，该研究使用混合分类-回归模型对经过偏差校正的GCM数据进行缩减，以每小时的分辨率生成未来的天气数据，以进行建筑能耗模拟。在这种情况下，混合模型被构造为组合模型，其中分类模型用作主模型，辅助模型用于数据超出观察值范围的情况。与以前的研究（使用人工生成的数据）相反，拟议的工作流程使用观测到的天气数据从历史数据中确定相似的天气模式，并使用其生成未来的天气数据。但是，在未来的GCM数据显示温度不在观测数据范围内的情况下，该研究应用了经过训练的回归模型来生成每小时天气数据。

拟议的工作流程使用户能够在不同的气候变化情景下逐年生成未来的天气文件，因此，保留了极端天气特征以进行极端或可靠性分析和设计优化。