A Machine Learning Framework for Objective Definition and Projection of Meteorological Seasons: An Application to the Indian Subcontinent

Abstract Meteorological seasons are typically defined through fixed calendar intervals or region‐specific thresholds, yet such approaches fail to capture the actual variability of the annual cycle. Here we present a machine learning framework that objectively detects meteorological seasons and projects their future evolution. The method combines Radially Constrained Clustering (RCC) to identify seasons from reanalysis data with a supervised Multinomial Logistic Regression Classifier (MLRC) that allows these definitions to be applied also to climate model projections. We use the Indian subcontinent as a detailed proof of concept region since it has established operational references for monsoon onset and withdrawal, and it includes contrasting seasonal regimes, from monsoon dominated lowlands to high mountain regions influenced by winter disturbances. The RCC‐derived seasons recover the main operational seasonal phases adopted by the India Meteorological Department, while also providing consistent definitions of transition periods. Evaluation against a CMIP6 multi‐model ensemble shows that models capture the seasonal cycle with high skill in Central and Southern India, but exhibit lower performance in complex mountainous regions. Future projections indicate a robust lengthening of the monsoon season, primarily due to delayed withdrawal, and a concurrent shortening of winter, driven by the loss of days in December–January. These findings highlight the asymmetric nature of seasonal shifts under global warming and demonstrate the potential of data‐driven approaches to provide objective and reproducible definitions of seasonality.