# DeepClimGAN: A High-Resolution Climate Data Generator

arXiv: https://arxiv.org/abs/2011.11705 · HTML: https://mlsanity.com/paper/2011.11705/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2020.

## Built on by (most cited first, 9 of 9)

- [DiffESM: Conditional Emulation of Temperature and Precipitation in Earth System Models with 3D Diffusion Models](https://mlsanity.com/paper/2409.11601/) (2024)
- [DiffESM: Conditional Emulation of Earth System Models with Diffusion Models](https://mlsanity.com/paper/2304.11699/) (2023)
- [COMET Flows: Towards Generative Modeling of Multivariate Extremes and Tail Dependence](https://mlsanity.com/paper/2205.01224/) (2022)
- [Beyond the Norm: A Survey of Synthetic Data Generation for Rare Events](https://mlsanity.com/paper/2506.06380/) (2025)
- [Loosely Conditioned Emulation of Global Climate Models With Generative Adversarial Networks](https://mlsanity.com/paper/2105.06386/) (2021)
- [A modular framework for extreme weather generation](https://mlsanity.com/paper/2102.04534/) (2021)
- [TemperatureGAN: Generative Modeling of Regional Atmospheric Temperatures](https://mlsanity.com/paper/2306.17248/) (2023)
- [A comparative study of stochastic and deep generative models for multisite precipitation synthesis](https://mlsanity.com/paper/2107.08074/) (2021)
- [Diffusion-Based Joint Temperature and Precipitation Emulation of Earth System Models](https://mlsanity.com/paper/2404.08797/) (2024)

## Abstract (arXiv)

Earth system models (ESMs), which simulate the physics and chemistry of the global atmosphere, land, and ocean, are often used to generate future projections of climate change scenarios. These models are far too computationally intensive to run repeatedly, but limited sets of runs are insufficient for some important applications, like adequately sampling distribution tails to characterize extreme events. As a compromise, emulators are substantially less expensive but may not have all of the complexity of an ESM. Here we demonstrate the use of a conditional generative adversarial network (GAN) to act as an ESM emulator. In doing so, we gain the ability to produce daily weather data that is consistent with what ESM might output over any chosen scenario. In particular, the GAN is aimed at representing a joint probability distribution over space, time, and climate variables, enabling the study of correlated extreme events, such as floods, droughts, or heatwaves.