Deep learning has been acknowledged as an increasingly important technology for ENSO forecasts. The most cutting-edge deep learning algorithm is developed based on Convolutional Neural Network (CNN), which can achieve a multi-year (about 17-month-lead) forecast and has conquered the ‘spring forecast barrier’ problem. However, this group of methods are still challenged by several critical issues. First, they usually utilize the global sea surface temperature (SST) fields as inputs without considering the specific contributions of variant oceanic regions in ENSO forecasts. Consequently, they cannot effectively investigate the role of the ‘teleconnection’ mechanism among different oceans (Indian, Pacific, and Atlantic Oceans) and different ocean parts (the tropic and non-tropic regions) especially in the forecast of extreme ENSO events. Second, existing methods mainly utilize the discrete monthly SST fields for Deep Learning for ENSO Forecasts ENSO forecasts without investigating the rate-of-changes between adjacent months, which also provides important information to the prediction of variation tendency. To solve these problems, this paper develops a Tendency-and-Attention-Informed Deep Residual Network (TA-DRN) for multi-year ENSO forecasts. The contributions of different oceanic regions can be learned by a spatial attention module while the variation tendency of adjacent previous and current months can be interpreted by the first-and-second order of differences of SST fields. Through informed by these two modules, the performance of TA-DRN can be improved significantly, especially in predicting extreme El Niño and La Niña events.