# Climate Variable Downscaling with Conditional Normalizing Flows

arXiv: https://arxiv.org/abs/2405.20719 · HTML: https://mlsanity.com/paper/2405.20719/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 31 May 2024.

## Builds on (most cited first, 6 of 6)

- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Learning Likelihoods with Conditional Normalizing Flows](https://mlsanity.com/paper/1912.00042/) (2019)
- [Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling](https://mlsanity.com/paper/2305.14452/) (2023)
- [ClimAlign: Unsupervised statistical downscaling of climate variables via normalizing flows](https://mlsanity.com/paper/2008.04679/) (2020)
- [Augmented Convolutional LSTMs for Generation of High-Resolution Climate Change Projections](https://mlsanity.com/paper/2009.11279/) (2020)
- [Physics-informed generative neural network: an application to troposphere temperature prediction](https://mlsanity.com/paper/2107.06991/) (2021)

## Built on by (most cited first, 3 of 3)

- [A Generative Framework for Probabilistic, Spatiotemporally Coherent Downscaling of Climate Simulation](https://mlsanity.com/paper/2412.15361/) (2024)
- [Weakly-Constrained 4D Var for Downscaling with Uncertainty using Data-Driven Surrogate Models](https://mlsanity.com/paper/2503.02665/) (2025)
- [A Probabilistic U-Net Approach to Downscaling Climate Simulations](https://mlsanity.com/paper/2511.03197/) (2025)

## Abstract (arXiv)

Predictions of global climate models typically operate on coarse spatial scales due to the large computational costs of climate simulations. This has led to a considerable interest in methods for statistical downscaling, a similar process to super-resolution in the computer vision context, to provide more local and regional climate information. In this work, we apply conditional normalizing flows to the task of climate variable downscaling. We showcase its successful performance on an ERA5 water content dataset for different upsampling factors. Additionally, we show that the method allows us to assess the predictive uncertainty in terms of standard deviation from the fitted conditional distribution mean.