Convergence at the Horizon towards Predictive Resilience in a Non-Stationary World

Climate change and technological advancement are driving a paradigm shift in the atmospheric and hydrometeorological sciences. This transformation is reflected in the complex interactions between ocean and atmospheric dynamics, the role of climate phenomena such as El Niño and La Niña, and the consequent effects on global water resources. This editorial argues that the critical task for the coming decade is to advance from hazard-focused forecasting toward the robust prediction of systemic impacts and societal resilience. We highlight three convergent frontiers of innovation: the integration of artificial intelligence and machine learning across the observational-modeling-prediction chain; the explicit coupling of human and water-energy-food systems within Earth system models; and the development of a “digital twin” framework for the Earth system. Success will require transcending disciplinary boundaries, promoting open science and data democratization, and fostering a new “convergence science” that interweaves physical dynamics, data science, socio-economics, and governance. The ultimate aim is to deliver actionable intelligence for anticipatory adaptation and enhanced climate resilience.