  Predicting sea surface temperature (SST) within the El Ni\~no-Southern
Oscillation (ENSO) region has been extensively studied due to its significant
influence on global temperature and precipitation patterns. Statistical models
such as linear inverse model (LIM), analog forecasting (AF), and recurrent
neural network (RNN) have been widely used for ENSO prediction, offering
flexibility and relatively low computational expense compared to large dynamic
models. However, these models have limitations in capturing spatial patterns in
SST variability or relying on linear dynamics. Here we present a modified
Convolutional Gated Recurrent Unit (ConvGRU) network for the ENSO region
spatio-temporal sequence prediction problem, along with the Ni\~no 3.4 index
prediction as a down stream task. The proposed ConvGRU network, with an
encoder-decoder sequence-to-sequence structure, takes historical SST maps of
the Pacific region as input and generates future SST maps for subsequent months
within the ENSO region. To evaluate the performance of the ConvGRU network, we
trained and tested it using data from multiple large climate models. The
results demonstrate that the ConvGRU network significantly improves the
predictability of the Ni\~no 3.4 index compared to LIM, AF, and RNN. This
improvement is evidenced by extended useful prediction range, higher Pearson
correlation, and lower root-mean-square error. The proposed model holds promise
for improving our understanding and predicting capabilities of the ENSO
phenomenon and can be broadly applicable to other weather and climate
prediction scenarios with spatial patterns and teleconnections.
