Factor Model-Based Detection of Regime Transitions in High-Dimensional Climate Data (ERA5)

Why It Matters

The findings demonstrate a sensitive, early‑warning approach to track structural climate change, offering valuable insight for adaptation planning and model improvement.

Key Takeaways

• Two stable climate regimes identified: thermal and moisture modes
• Factor 1 variance rose from 50.1% to 54.2%
• Factor 2 variance fell from 27.5% to 20.3%
• Cross‑loading of dew point indicates early structural shift
• Multivariate factor analysis detects gradual regime reweighting

Pulse Analysis

Traditional climate assessments rely on single‑variable trends such as temperature rise or precipitation change. While those metrics capture the magnitude of warming, they often miss subtle reorganizations in the underlying dynamics of the system. Exploratory Factor Analysis (EFA) offers a multivariate lens, extracting latent structures that govern how variables co‑vary over time. By applying EFA to the ERA5 reanalysis dataset for a site at 29° N, 77° E, researchers can monitor shifts in the hidden covariance matrix, providing an early‑warning signal of structural climate change that univariate methods overlook.

The analysis uncovered two persistent regimes: a thermally driven land‑atmosphere mode (Factor 1) and a moisture‑circulation mode (Factor 2). Between 1991 and 2025, Factor 1’s explained variance climbed from 50.1 % to 54.2 %, while Factor 2’s share dropped from 27.5 % to 20.3 %, indicating a gradual reweighting toward thermal processes. Moreover, the dew‑point temperature began to load on both factors, a cross‑loading pattern that signals emerging inter‑mode coupling before a full regime transition becomes evident.

These results suggest that climate evolution at the studied location proceeds through amplification of existing latent structures rather than abrupt collapse. Monitoring factor loadings and variance redistribution can therefore serve as a sensitive diagnostic for early detection of regime shifts, informing adaptation strategies and guiding targeted climate‑model refinements. The methodology is scalable to other high‑dimensional climate datasets, offering policymakers and scientists a quantitative tool to track the subtle reorganization of atmospheric and surface processes in a warming world.