  A significant challenge in seasonal climate prediction is whether a
prediction can beat climatology. We hereby present results from two data-driven
models - a convolutional (CNN) and a recurrent (RNN) neural network - that
predict 2 m temperature out to 52 weeks for six geographically-diverse
locations. The motivation for testing the two classes of ML models is to allow
the CNN to leverage information related to teleconnections and the RNN to
leverage long-term historical temporal signals. The ML models boast improved
accuracy of long-range temperature forecasts up to a lead time of 30 weeks for
PCC and up 52 weeks for RMSESS, however only for select locations. Further
iteration is required to ensure the ML models have value beyond regions where
the climatology has a noticeably reduced correlation skill, namely the tropics.
