  Extreme precipitation wreaks havoc throughout the world, causing billions of
dollars in damage and uprooting communities, ecosystems, and economies.
Accurate extreme precipitation prediction allows more time for preparation and
disaster risk management for such extreme events. In this paper, we focus on
short-term extreme precipitation forecasting (up to a 12-hour ahead-of-time
prediction) from a sequence of sea level pressure and zonal wind anomalies.
Although existing machine learning approaches have shown promising results, the
associated model and climate uncertainties may reduce their reliability. To
address this issue, we propose a self-attention augmented convolution mechanism
for extreme precipitation forecasting, systematically combining attention
scores with traditional convolutions to enrich feature data and reduce the
expected errors of the results. The proposed network architecture is further
fused with a highway neural network layer to gain the benefits of unimpeded
information flow across several layers. Our experimental results show that the
framework outperforms classical convolutional models by 12%. The proposed
method increases machine learning as a tool for gaining insights into the
physical causes of changing extremes, lowering uncertainty in future forecasts.
