  Numerical weather prediction (NWP) models struggle to skillfully predict
tropical precipitation occurrence and amount, calling for alternative
approaches. For instance, it has been shown that fairly simple, purely
data-driven logistic regression models for 24-hour precipitation occurrence
outperform both climatological and NWP forecasts for the West African summer
monsoon. More complex neural network based approaches, however, remain
underdeveloped due to the non-Gaussian character of precipitation. In this
study, we develop, apply and evaluate a novel two-stage approach, where we
train a U-Net convolutional neural network (CNN) model on gridded rainfall data
to obtain a deterministic forecast and then apply the recently developed,
nonparametric Easy Uncertainty Quantification (EasyUQ) approach to convert it
into a probabilistic forecast. We evaluate CNN+EasyUQ for one-day ahead 24-hour
accumulated precipitation forecasts over northern tropical Africa for
2011--2019, with the Integrated Multi-satellitE Retrievals for GPM (IMERG) data
serving as ground truth. In the most comprehensive assessment to date we
compare CNN+EasyUQ to state-of-the-art physics-based and data-driven approaches
such as a monthly probabilistic climatology, raw and postprocessed ensemble
forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF),
and traditional statistical approaches that use up to 25 predictor variables
from IMERG and the ERA5 reanalysis.Generally, statistical approaches perform
about en par with post-processed ECMWF ensemble forecasts. The CNN+EasyUQ
approach, however, clearly outperforms all competitors for both occurrence and
amount. Hybrid methods that merge CNN+EasyUQ and physics-based forecasts show
slight further improvement. Thus, the CNN+EasyUQ approach can likely improve
operational probabilistic forecasts of rainfall in the tropics, and potentially
even beyond.
