# SoftSEEPS improves ML-based precipitation forecasting

arXiv: https://arxiv.org/abs/2610.09752 · HTML: https://mlsanity.com/paper/2610.09752/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 7 Oct 2026.

## Builds on (most cited first, 5 of 5)

- [WeatherBench: A benchmark dataset for data-driven weather forecasting](https://mlsanity.com/paper/2002.00469/) (2020)
- [GenCast: Diffusion-based ensemble forecasting for medium-range weather](https://mlsanity.com/paper/2312.15796/) (2023)
- [FuXi-2.0: Advancing machine learning weather forecasting model for practical applications](https://mlsanity.com/paper/2409.07188/) (2024)
- [ArchesWeather: An efficient AI weather forecasting model at 1.5deg resolution](https://mlsanity.com/paper/2405.14527/) (2024)
- [WeatherMesh-3: Fast and accurate operational global weather forecasting](https://mlsanity.com/paper/2503.22235/) (2025)

## Abstract (arXiv)

In this paper we have developed a differentiable approximation of the well-known SEEPS score, which we name SoftSEEPS. This allows the training of a Machine Learning model to forecast precipitation directly. We test SoftSEEPS on the IMERG dataset (0.1 degree resolution) by training a decoder for precipitation on the latent space of a pre-trained low-resolution forecasting model. Combining SoftSEEPS and RMSE in a joint objective is possible with marginal trade-offs in either metric.