[2510.22855] A Review of Neural Networks in Precipitation Prediction

Computer Science > Machine Learning arXiv:2510.22855 (cs) [Submitted on 26 Oct 2025 (v1), last revised 1 Apr 2026 (this version, v2)] Title:A Review of Neural Networks in Precipitation Prediction Authors:Yugong Zeng, Jiayuan Wang, Jonathan Wu View a PDF of the paper titled A Review of Neural Networks in Precipitation Prediction, by Yugong Zeng and 2 other authors View PDF HTML (experimental) Abstract:Precipitation prediction has undergone a profound transformation. A notable limitation of traditional NWP is the need for extensive statistical post-processing. To address this challenge, neural network-based approaches were developed. These approaches offer a framework that directly learns the mapping from atmospheric predictors to precipitation targets. Based on the technological development, this article first reviews the traditional precipitation forecasting methods and summarizes the development trends of precipitation forecasting based on neural networks. We then outline the training process, loss functions, and some datasets for precipitation prediction. In the main body of the article, we detail the basic artificial neural networks (ANNs), spatial feature extraction models, time feature extraction models, generative models, Transformer models, graph neural networks (GNNs), and emerging hybrid models. Finally, in the appendix, we supplement the commonly used evaluation metrics. This paper focuses on the advantages and disadvantages of various neural network models in pr...