Traditional El Niño Southern Oscillation indices, like the Niño 3.4 index, although well-predicted themselves, fail to offer reliable sub-seasonal to seasonal predictions of Western US precipitation. A novel Multi-Input Multi-Output Autoencoder-decoder (MIMO-AE) machine learning network is constructed to capture the non-linear relationship between Southern California precipitation and Tropical Pacific Ocean sea surface temperatures. The MIMO-AE is trained on both monthly Tropical Pacific SST anomalies and Southern California precipitation anomalies simultaneously. The co-variability of the two fields in the MIMO-AE shared nonlinear latent space can be condensed into an index, termed the MIMO-AE index. A transfer learning approach is used to train a MIMO-AE on the combined dataset of 100 years of output from a historical simulation with DOE’s Energy Exascale Earth Systems Model (E3SMv1) and a segment of observational data. Long Short-Term Memory networks are used to assess sub-seasonal to seasonal predictability of Southern California precipitation from the MIMO-AE index. MIMO-AE provides enhanced predictability of SC-PRECIP for a lead-time of up to four months as compared to Niño 3.4 index and the El Niño Southern Oscillation Longitudinal Index. This is likely because MIMO-AE learned SST anomaly patterns associated with Southern California precipitation strongly influence processes that drive precipitation over the region, lending predictive skill.