# Extreme Precipitation Seasonal Forecast Using a Transformer Neural Network

arXiv: https://arxiv.org/abs/2107.06846 · HTML: https://mlsanity.com/paper/2107.06846/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 16 references that are in the map.
- First posted on arXiv 14 Jul 2021.

## Builds on (most cited first, 1 of 1)

- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)

## Built on by (most cited first, 2 of 2)

- [Extreme precipitation forecasting using attention augmented convolutions](https://mlsanity.com/paper/2201.13408/) (2022)
- [Interpretable Water Level Forecaster with Spatiotemporal Causal Attention Mechanisms](https://mlsanity.com/paper/2303.00515/) (2023)

## Abstract (arXiv)

An impact of climate change is the increase in frequency and intensity of extreme precipitation events. However, confidently predicting the likelihood of extreme precipitation at seasonal scales remains an outstanding challenge. Here, we present an approach to forecasting the quantiles of the maximum daily precipitation in each week up to six months ahead using the temporal fusion transformer (TFT) model. Through experiments in two regions, we compare TFT predictions with those of two baselines: climatology and a calibrated ECMWF SEAS5 ensemble forecast (S5). Our results show that, in terms of quantile risk at six month lead time, the TFT predictions significantly outperform those from S5 and show an overall small improvement compared to climatology. The TFT also responds positively to departures from normal that climatology cannot.