# A Review of Neural Networks in Precipitation Prediction

arXiv: https://arxiv.org/abs/2510.22855 · HTML: https://mlsanity.com/paper/2510.22855/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 169 references that are in the map.
- First posted on arXiv 26 Oct 2025.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators](https://mlsanity.com/paper/2202.11214/) (2022)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [WeatherBench: A benchmark dataset for data-driven weather forecasting](https://mlsanity.com/paper/2002.00469/) (2020)
- [Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model](https://mlsanity.com/paper/1706.03458/) (2017)
- [Earthformer: Exploring Space-Time Transformers for Earth System Forecasting](https://mlsanity.com/paper/2207.05833/) (2022)
- [PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning](https://mlsanity.com/paper/1804.06300/) (2018)
- [Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics](https://mlsanity.com/paper/1811.07490/) (2018)
- [Transformers in Remote Sensing: A Survey](https://mlsanity.com/paper/2209.01206/) (2022)
- [Increasing the accuracy and resolution of precipitation forecasts using deep generative models](https://mlsanity.com/paper/2203.12297/) (2022)
- [Precipitation Nowcasting Using Diffusion Transformer with Causal Attention](https://mlsanity.com/paper/2410.13314/) (2024)
- [Towards a Spatiotemporal Fusion Approach to Precipitation Nowcasting](https://mlsanity.com/paper/2505.19258/) (2025)

## Abstract (arXiv)

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 precipitation forecasting applications, and also pays attention to the latest progress of neural network-based methods. Overall, neural networks have significantly improved the accuracy of short-term and medium-term precipitation forecasting, but still face challenges in representing extreme rainfall, handling imbalanced data, and ensuring physical consistency. The latest progress shows that future prediction systems will increasingly rely on the integration of multiple sources of data and hybrid physical-data-driven models to enhance their robustness and applicability. By compositing research covering multiple eras and paradigms, we not only depict the history of neural networks in precipitation prediction but also outline future directions in next generation forecasting systems.