# Fast, Scale-Adaptive, and Uncertainty-Aware Downscaling of Earth System Model Fields with Generative Machine Learning

arXiv: https://arxiv.org/abs/2403.02774 · HTML: https://mlsanity.com/paper/2403.02774/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Placed in Flow matching, time series and science, one of 14 directions in Diffusion models.
- Builds on 34 of its 66 references that are in the map.
- First posted on arXiv 5 Mar 2024.

## Placed in

- Diffusion models / [Flow matching, time series and science](https://mlsanity.com/diffusion/flow-matching-time-series-and-science/)

## Builds on (most cited first, 30 of 34)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Elucidating the Design Space of Diffusion-Based Generative Models](https://mlsanity.com/paper/2206.00364/) (2022)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [Consistency Models](https://mlsanity.com/paper/2303.01469/) (2023)
- [Normalizing Flows for Probabilistic Modeling and Inference](https://mlsanity.com/paper/1912.02762/) (2019)
- [NICE: Non-linear Independent Components Estimation](https://mlsanity.com/paper/1410.8516/) (2014)
- [On the Variance of the Adaptive Learning Rate and Beyond](https://mlsanity.com/paper/1908.03265/) (2019)
- [Adversarial Diffusion Distillation](https://mlsanity.com/paper/2311.17042/) (2023)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Towards Principled Methods for Training Generative Adversarial Networks](https://mlsanity.com/paper/1701.04862/) (2017)
- [Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed](https://mlsanity.com/paper/2101.02388/) (2021)
- [Skillful Precipitation Nowcasting using Deep Generative Models of Radar](https://mlsanity.com/paper/2104.00954/) (2021)
- [GenCast: Diffusion-based ensemble forecasting for medium-range weather](https://mlsanity.com/paper/2312.15796/) (2023)
- [Fast Training of Diffusion Models with Masked Transformers](https://mlsanity.com/paper/2306.09305/) (2023)
- [Generative Modelling With Inverse Heat Dissipation](https://mlsanity.com/paper/2206.13397/) (2022)
- [Text-to-Image Diffusion Models are Zero-Shot Classifiers](https://mlsanity.com/paper/2303.15233/) (2023)
- [A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts](https://mlsanity.com/paper/2204.02028/) (2022)
- [AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning](https://mlsanity.com/paper/2308.13280/) (2023)
- [Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models](https://mlsanity.com/paper/2305.15618/) (2023)
- [Hard-Constrained Deep Learning for Climate Downscaling](https://mlsanity.com/paper/2208.05424/) (2022)
- [Physically Constrained Generative Adversarial Networks for Improving Precipitation Fields from Earth System Models](https://mlsanity.com/paper/2209.07568/) (2022)

## Built on by (most cited first, 27 of 27)

- [Conditional diffusion models for downscaling and bias correction of Earth system model precipitation](https://mlsanity.com/paper/2404.14416/) (2024)
- [Machine learning emulation of precipitation from km-scale UK regional climate simulations using a diffusion model](https://mlsanity.com/paper/2407.14158/) (2024)
- [A Generative Framework for Probabilistic, Spatiotemporally Coherent Downscaling of Climate Simulation](https://mlsanity.com/paper/2412.15361/) (2024)
- [EnScale: Temporally-consistent multivariate generative downscaling via proper scoring rules](https://mlsanity.com/paper/2509.26258/) (2025)
- [ArchesClimate: Probabilistic Decadal Ensemble Generation With Flow Matching](https://mlsanity.com/paper/2509.15942/) (2025)
- [Score-based generative emulation of impact-relevant Earth system model outputs](https://mlsanity.com/paper/2510.04358/) (2025)
- [WIND: Weather Inverse Diffusion for Zero-Shot Atmospheric Modeling](https://mlsanity.com/paper/2602.03924/) (2026)
- [Physics-Constrained Adaptive Flow Matching for Climate Downscaling](https://mlsanity.com/paper/2604.03459/) (2026)
- [Generating realistic global precipitation fields from modelled atmospheric circulation](https://mlsanity.com/paper/2504.00307/) (2025)
- [Generative artificial intelligence improves projections of climate extremes](https://mlsanity.com/paper/2508.16396/) (2025)
- [Probabilistic Super-Resolution for Urban Micrometeorology via a Schr"odinger Bridge](https://mlsanity.com/paper/2510.12148/) (2025)
- [A unified multimodal understanding and generation model for cross-disciplinary scientific research](https://mlsanity.com/paper/2601.01363/) (2026)
- [Super-Resolving Coarse-Resolution Weather Forecasts With Flow Matching](https://mlsanity.com/paper/2604.00897/) (2026)
- [Data-driven Mesoscale Weather Forecasting Combining Swin-Unet and Diffusion Models](https://mlsanity.com/paper/2503.19354/) (2025)
- [Physics-constrained generative machine learning-based high-resolution downscaling of Greenland's surface mass balance and surface temperature](https://mlsanity.com/paper/2507.22485/) (2025)
- [AODDiff: Probabilistic Reconstruction of Aerosol Optical Depth via Diffusion-based Bayesian Inference](https://mlsanity.com/paper/2512.24847/) (2025)
- [Zero-Shot Statistical Downscaling via Diffusion Posterior Sampling](https://mlsanity.com/paper/2601.21760/) (2026)
- [Generative Unsupervised Downscaling of Climate Models via Domain Alignment: Application to Wind Fields](https://mlsanity.com/paper/2604.03341/) (2026)
- [WindINR: Latent-State INR for Fast Local Wind Query and Correction in Complex Terrain](https://mlsanity.com/paper/2605.09511/) (2026)
- [Generative climate downscaling enables high-resolution compound risk assessment by preserving multivariate dependencies](https://mlsanity.com/paper/2605.11531/) (2026)
- [Longwang: Zero-Shot Global Spatiotemporal Precipitation Downscaling with a Latent Generative Prior](https://mlsanity.com/paper/2605.17603/) (2026)
- [Temporal Coverage over Density: Parsimonious Training-Set Design for ML Climate Downscaling](https://mlsanity.com/paper/2606.07898/) (2026)
- [Physics-Guided Dual Decoding and Spectral Supervision for Global 3D Hydrometeor Prediction](https://mlsanity.com/paper/2606.08563/) (2026)
- [Regional Climate Model Emulation with Diffusion Approaches: What is the Added Value of Generative Machine Learning?](https://mlsanity.com/paper/2606.14570/) (2026)
- [Transferable Dual-Stream Representations for Mesoscale-Preserving Sea Surface Temperature Downscaling](https://mlsanity.com/paper/2608.04230/) (2026)
- [RainAtlas: A Multi-Continental Dataset for Precipitation Downscaling](https://mlsanity.com/paper/2609.39833/) (2026)
- [Generalizable Neural Downscaling of Earth System Model Wind Fields via Continuous Dynamics Modeling](https://mlsanity.com/paper/2610.03757/) (2026)

## Abstract (arXiv)

Accurate and high-resolution Earth system model (ESM) simulations are essential to assess the ecological and socio-economic impacts of anthropogenic climate change, but are computationally too expensive to be run at sufficiently high spatial resolution. Recent machine learning approaches have shown promising results in downscaling ESM simulations, outperforming state-of-the-art statistical approaches. However, existing methods require computationally costly retraining for each ESM and extrapolate poorly to climates unseen during training. We address these shortcomings by learning a consistency model (CM) that efficiently and accurately downscales arbitrary ESM simulations without retraining in a zero-shot manner. Our approach yields probabilistic downscaled fields at a resolution only limited by the observational reference data. We show that the CM outperforms state-of-the-art diffusion models at a fraction of computational cost while maintaining high controllability on the downscaling task. Further, our method generalizes to climate states unseen during training without explicitly formulated physical constraints.