Data-driven monitoring strategies are essential for designing, operating, and maintaining infrastructure. These strategies use historical data to model patterns and relationships between system variables and environmental factors, such as temperature and humidity. However, climate change is altering these factors, causing a divergence between past data and current conditions, which can undermine monitoring strategies and lead to inaccurate predictions and increased risks. This research proposes a data-driven monitoring framework to analyze and forecast structural responses to environmental changes. The approach integrates system identification methods, Gaussian Process Regression (GPR), and climate projections. Structural behavior is characterized in terms of modal parameters using automated time-domain system identification techniques.