Moisture-related damage in buildings is a significant and costly issue, often originating from the design phase. Hygrothermal simulation tools are widely used to predict future temperature and moisture conditions in building envelopes, but the reliability of these simulations depends heavily on the quality of the input data. Therefore, using credible and well-founded input parameters is essential for accurate predictions. To improve the precision of these simulations and enable meaningful comparisons with real-world measurements, this study presents a method for evaluating and ranking calculated values of temperature, relative humidity, and moisture content. The proposed method introduces an analytical performance metric function that accounts for time shifts, percentiles, root mean square (RMS) values and applies a frequency-weighing filter to residuals. By using long-term measurement data from occupied buildings, the method identifies which parameters most significantly influence simulation accuracy. This approach not only enhances understanding of model sensitivity but also supports the development of more reliable input data. As a result, it contributes to more accurate moisture risk assessments—such as mold growth predictions—and promotes the design of more moisture-safe and durable building solutions.