  Deep learning-based time series forecasting has dominated the short-term
precipitation forecasting field with the help of its ability to estimate motion
flow in high-resolution datasets. The growing interest in precipitation
nowcasting offers substantial opportunities for the advancement of current
forecasting technologies. Nevertheless, there has been a scarcity of in-depth
surveys of time series precipitation forecasting using deep learning. Thus,
this paper systemically reviews recent progress in time series precipitation
forecasting models. Specifically, we investigate the following key points
within background components, covering: i) preprocessing, ii) objective
functions, and iii) evaluation metrics. We then categorize forecasting models
into \textit{recursive} and \textit{multiple} strategies based on their
approaches to predict future frames, investigate the impacts of models using
the strategies, and performance assessments. Finally, we evaluate current deep
learning-based models for precipitation forecasting on a public benchmark,
discuss their limitations and challenges, and present some promising research
directions. Our contribution lies in providing insights for a better
understanding of time series precipitation forecasting and in aiding the
development of robust AI solutions for the future.
