Factor Model-based Detection of Regime Transitions in High-dimensional Climate Data (ERA5)

Climate change is usually assessed by examining trends in individual variables, but changes in relationships among variables may indicate deeper structural shifts within climate systems. Here, using Exploratory Factor Analysis across five periods from 1991 to 2025 for a location at longitude 29°N, latitude 77°E, we demonstrate that this multivariate approach can detect such structural changes by identifying temporal shifts in hidden covariance patterns. Across all periods, two consistent climate regimes are identified—a thermally driven land-atmosphere mode (Factor 1) and a moisture-circulation mode (Factor 2)—showing stable core modes of variability. However, systematic redistribution of explained variance (Factor 1 increasing from 50.1% to 54.2%; Factor 2 declining from 27.5% to 20.3%) and changing loading magnitudes reveal gradual reweighting within these regimes. The thermal mode becomes more dominant as surface-boundary layer coupling weakens, while the moisture mode shows ongoing decline through reduced influence of meridional wind and humidity variables, suggesting that climate variability is reorganizing around thermally driven processes. Notably, emerging cross-loading behavior in dew point temperature—showing increasing association with both factors—suggests that structural realignment may begin at the variable level before fully manifesting as regime changes. These findings show that climate change at this location proceeds through the amplification or reduction of existing latent structures rather than sudden structural collapse. By monitoring changes in how variables relate to each other through factor loading matrices and variance redistribution, this multivariate approach offers a sensitive way to detect early signs of structural climate change in complex environmental systems.