Improving predictions of decadal climate variability is critical for reducing uncertainty in near-term climate change. Here we investigate the potential to improve prediction skill in the North Pacific by identifying predictable patterns of sea surface temperatures (SSTs) in climate simulations, and then applying them to observations. A convolutional neural network (CNN) is first trained to predict basin-wide SSTs in the North Pacific on 1-5 year time-scales in nine global climate models (GCMs), and a pattern of high skill is identified from the GCM data. This pattern of high skill learned from GCMs is then skillfully predicted by the CNN when given observations as inputs. The identified pattern is notably not the Pacific Decadal Oscillation, and instead corresponds to basinwide warming and cooling focused in the North Pacific Gyre. We conclude that investigating the mechanisms that contribute to predictability (rather than variability) is an effective avenue for improving near-term climate predictions.