Tracking Seasonal Transitions Using a Meteorological Seasonality Index

Seasonal boundaries are commonly defined using fixed calendar conventions or climatology‐based thresholds, which provide practical but static representations of seasons. Here, we introduce the Normalised Daily Temperature Range (NDTR), a physically based seasonality index that captures transitions between atmospheric regimes using routine temperature observations. NDTR is applied at site and regional scales using ERA5‐Land reanalysis data to derive season start, end and duration from the annual cycle of near‐surface temperature. Seasonal boundaries are identified from extreme values and inflection points of the smoothed NDTR curve. The resulting classification is evaluated through (i) internal meteorological consistency using air temperature, relative humidity (RH) and dew‐point variability and (ii) external coherence with independent vegetation indicators (LAI, EVI) and observed phenology. Compared with calendar‐based seasons, NDTR‐defined seasons reduce intra‐seasonal variability in temperature and dew‐point metrics by 12%–14%, with reductions up to 28% during transition seasons. These improvements are most pronounced in spring and autumn, while summer and winter are more stable when season duration is accounted for. External validation shows that NDTR‐derived growing season onset (SOS) occurs later than observed, with median differences of approximately 10 days at low elevations, decreasing to 3–4 days at mid‐elevations. In contrast, the end of the growing season (EOS) shows larger and more variable offsets, increasing from 12 days above 700 m to 22.8 days at low elevations, with NDTR‐derived EOS occurring earlier than observed. This pattern reflects a delayed ecosystem response rather than inconsistencies in atmospheric season timing. The NDTR reveals systematic gradients in seasonal duration across the Euro‐Mediterranean region, with winter duration decreasing by 6–21 days and summer duration increasing by 4–12 days. NDTR provides a continuous, threshold‐independent framework for identifying seasonal transitions across climates, independent of biosphere‐specific signals. It enables monitoring and forecasting of seasonal dynamics for applications in climate analysis and climate‐sensitive health systems.