Accurate weather data is essential for building energy modeling (BEM), yet the actual urban local weather condition is often overlooked. This study developed an approach to generate local weather data using ERA5, a global atmospheric reanalysis dataset as input for two urban land surface models, Urban Tethys-Chloris (UT&C) and Urban Weather Generator (UWG). The generated datasets (UT&C-ERA5 and UWG-ERA5) are compared to locally measured weather data for a university campus in Singapore. Results show that the original ERA5 underestimates the diurnal temperature range. UT&C-ERA5 significantly improves hourly dry bulb temperature, reducing Mean Absolute Error (MAE) from 1.73 to 1.32 and Root Mean Square Error (RMSE) from 2.31 to 1.67, while UWG-ERA5 shows modest improvements (MAE from 1.73 to 1.70, RMSE from 2.31 to 2.22). UT&C-ERA5 also improves wind speed, lowering MAE from 2.85 to 1.54 and RMSE from 3.23 to 1.79. Subsequently, these weather datasets are employed as inputs for a calibrated BEM. Compared to the original ERA5, UT&C-ERA5 reduces CV (RMSE) of building cooling load from 17.13 % to 15.45 %. By leveraging the global availability of atmospheric reanalysis datasets, this approach can support building energy design and improve energy efficiency in tropical cities.