Building energy simulation models are indispensable tools for predicting thermal and energy performance and evaluating building energy efficiency. However, in the calibration and sensitivity analysis of these models, most studies focus on air temperatures or energy consumption, typically not taking into account critical parameters such as surface temperatures, convective heat transfer coefficients, and thermal and solar absorptivities. In this context, this work complements prior studies by incorporating these critical parameters, including convection coefficients and thermal and solar absorptivity, enhancing both the reliability and completeness of building simulation models. Using a monitoring period, air and surface temperature data were collected under free-floating conditions and supplemented with meteorological records from an on-site station. Optimization was performed using the root mean square error (RMSE) metric to minimize discrepancies between measured and simulated values of zone air and surface temperatures. The results demonstrate that the detailed calibration strategy, which considers convective coefficients and material absorptivities as design variables and minimizes errors in both air and surface temperature predictions, significantly enhances model accuracy. This approach reduces the RMSE of air temperature predictions by 60% and the RMSE of surface temperature predictions by 73% (walls), 79% (inner roof), 42% (outer roof), and 82% (floor). Further analysis of heat gains and losses emphasizes the critical role of these parameters in the accuracy in the modeling of building-environment interactions. This detailed and robust approach ensures a more precise and reliable simulation model, highlighting the critical role of advanced calibration techniques in optimizing building energy performance simulations.