This team, which is composed of experts from the Department of Atmospheric and Oceanic Sciences at the University of Colorado Boulder and the Karamperidou Research Group at the Department of Atmospheric Sciences, University of Hawai‘i at Mānoa, combines deep knowledge in ENSO prediction using both dynamical models and machine learning techniques. Their research has made significant strides in enhancing the scientific understanding of the dynamics behind extreme El Niño events and prolonged La Niña episodes. These advancements have been translated into a powerful suite of predictive tools designed specifically to forecast disruptive ENSO events. A key achievement of their work was the successful prediction of the second-year La Niña of 2017–2018, which was closely linked to the exceptionally active and costly 2017 Atlantic hurricane season. Their models have demonstrated a strong ability to reduce false positives — one of the persistent challenges in operational forecasting models. These results underscore the real-world applicability and accuracy of these predictive approaches.