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

A Machine Learning Framework for Objective Definition and Projection of Meteorological Seasons: an Application to the Indian Subcontinent | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 19 November 2025 V1 Latest version Share on A Machine Learning Framework for Objective Definition and Projection of Meteorological Seasons: an Application to the Indian Subcontinent Authors : Jacopo Grassi 0009-0001-3023-6976 [email protected] , Elisa Palazzi 0000-0003-1683-5267 , Jost von Hardenberg 0000-0002-5312-8070 , and Paolo Davini Authors Info & Affiliations https://doi.org/10.22541/au.176357597.77420190/v1 252 views 94 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share 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 temporally contiguous seasons from reanalysis data with a supervised Multinomial Logistic Regression Classifier (MLRC) that allows to apply these definitions also to climate model projections. We apply this framework to the Indian subcontinent, a region dominated by the Indian Summer Monsoon (ISM) and influenced by Western Disturbances during winter. The RCC-derived seasons closely reproduce the operational partition 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, reproducible, and transferable definitions of seasonality. While the case study focuses on South Asia, the methodology is general and can be applied to other regions to support climate services and adaptation planning. Supplementary Material File (machine_learning_framework_seasons.pdf) Download 2.33 MB Information & Authors Information Version history V1 Version 1 19 November 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords climatology (global change) indian summer monsoon machine learning meteorological seasons Authors Affiliations Jacopo Grassi 0009-0001-3023-6976 [email protected] Politecnico di Torino View all articles by this author Elisa Palazzi 0000-0003-1683-5267 University of Turin View all articles by this author Jost von Hardenberg 0000-0002-5312-8070 Politecnico di Torino View all articles by this author Paolo Davini Consiglio Nazionale delle Ricerche, Istituto di Scienze dell'Atmosfera e del Clima (CNR-ISAC) View all articles by this author Metrics & Citations Metrics Article Usage 252 views 94 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Jacopo Grassi, Elisa Palazzi, Jost von Hardenberg, et al. A Machine Learning Framework for Objective Definition and Projection of Meteorological Seasons: an Application to the Indian Subcontinent. Authorea . 19 November 2025. DOI: https://doi.org/10.22541/au.176357597.77420190/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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