Combining Neural Networks and CMIP6 Simulations to Learn Windows of Opportunity for Skillful Prediction of Multiyear Sea Surface Temperature Variability

• Neural networks can learn predictable signals of internal sea surface temperature variability at 1-3, 1-5, and 3-7 year lead times • Neural networks trained on climate model output can skillfully predict sea surface temperature variability in reconstructed observations • Neural network skill in predicting observed sea surface temperature variability depends on the climate model used for training