# Deep Learning for Climate Model Output Statistics

arXiv: https://arxiv.org/abs/2012.10394 · HTML: https://mlsanity.com/paper/2012.10394/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2020.

## Built on by (most cited first, 1 of 1)

- [Self-Attentive Ensemble Transformer: Representing Ensemble Interactions in Neural Networks for Earth System Models](https://mlsanity.com/paper/2106.13924/) (2021)

## Abstract (arXiv)

Climate models are an important tool for the assessment of prospective climate change effects but they suffer from systematic and representation errors, especially for precipitation. Model output statistics (MOS) reduce these errors by fitting the model output to observational data with machine learning. In this work, we explore the feasibility and potential of deep learning with convolutional neural networks (CNNs) for MOS. We propose the CNN architecture ConvMOS specifically designed for reducing errors in climate model outputs and apply it to the climate model REMO. Our results show a considerable reduction of errors and mostly improved performance compared to three commonly used MOS approaches.