  The prediction of tropical rain rates from atmospheric profiles poses
significant challenges, mainly due to the heavy-tailed distribution exhibited
by tropical rainfall. This study introduces over-parameterized neural networks
not only to forecast tropical rain rates, but also to explain their
heavy-tailed distribution. The prediction is separately conducted for three
rain types (stratiform, deep convective, and shallow convective) observed by
the Global Precipitation Measurement satellite radar over the West and East
Pacific regions. Atmospheric profiles of humidity, temperature, and zonal and
meridional winds from the MERRA-2 reanalysis are considered as features.
Although over-parameterized neural networks are well-known for their ``double
descent phenomenon," little has been explored about their applicability to
climate data and capability of capturing the tail behavior of data. In our
results, over-parameterized neural networks accurately predict the rain rate
distributions and outperform other machine learning methods. Spatial maps show
that over-parameterized neural networks also successfully describe spatial
patterns of each rain type across the tropical Pacific. In addition, we assess
the feature importance for each over-parameterized neural network to provide
insight into the key factors driving the predictions, with low-level humidity
and temperature variables being the overall most important. These findings
highlight the capability of over-parameterized neural networks in predicting
the distribution of the rain rate and explaining extreme values.
