Advanced Weather Data Morphing For Future Climate-Based Building Simulation. A Modular Python Tool Utilizing Enhanced Morphing Algorithms For EPW File Generation

Climate change poses significant challenges for building design and performance simulation, requiring accurate future weather data. This paper presents a Python-based tool for generating future weather files using state-of-the-art morphing techniques. The tool implements the Bounded Temperature Weighted Stretch algorithm for dry bulb temperature and solar radiation, while utilizing enhanced Belcher morphing methods for other climate variables. Testing with Vienna weather data under the SSP370 scenario for 2041-2060 revealed notable projected changes: a slight mean temperature decrease of $0.5^{\circ} \mathrm{C}$, reduced precipitation (-35.4%) especially during summer months and decreased solar radiation (-6.3%). Validation identified limitations in maintaining consistency between solar radiation components that require further refinement. The tool incorporates CMIP6 climate projections and high-resolution historical data, enabling more accurate future weather simulations for building design. This research advances morphing methodologies by implementing recent algorithmic improvements and utilizing current climate scenarios, contributing to more resilient building design under changing climate conditions.