# FuXi-Extreme: Improving extreme rainfall and wind forecasts with diffusion model

arXiv: https://arxiv.org/abs/2310.19822 · HTML: https://mlsanity.com/paper/2310.19822/

## Key facts

- Cited by 22 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 18 of its 45 references that are in the map.
- First posted on arXiv 25 Oct 2023.

## 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, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Image Super-Resolution via Iterative Refinement](https://mlsanity.com/paper/2104.07636/) (2021)
- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [Cascaded Diffusion Models for High Fidelity Image Generation](https://mlsanity.com/paper/2106.15282/) (2021)
- [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022)
- [FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators](https://mlsanity.com/paper/2202.11214/) (2022)
- [GraphCast: Learning skillful medium-range global weather forecasting](https://mlsanity.com/paper/2212.12794/) (2022)
- [Deblurring via Stochastic Refinement](https://mlsanity.com/paper/2112.02475/) (2021)
- [FuXi: A cascade machine learning forecasting system for 15-day global weather forecast](https://mlsanity.com/paper/2306.12873/) (2023)
- [WeatherBench 2: A benchmark for the next generation of data-driven global weather models](https://mlsanity.com/paper/2308.15560/) (2023)
- [FractalNet: Ultra-Deep Neural Networks without Residuals](https://mlsanity.com/paper/1605.07648/) (2016)
- [Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges](https://mlsanity.com/paper/2010.09337/) (2020)
- [SwinRDM: Integrate SwinRNN with Diffusion Model towards High-Resolution and High-Quality Weather Forecasting](https://mlsanity.com/paper/2306.03110/) (2023)

## Built on by (most cited first, 22 of 22)

- [Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey](https://mlsanity.com/paper/2312.03014/) (2023)
- [FuXi Weather: A data-to-forecast machine learning system for global weather](https://mlsanity.com/paper/2408.05472/) (2024)
- [ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast](https://mlsanity.com/paper/2402.01295/) (2024)
- [FuXi-ENS: A machine learning model for medium-range ensemble weather forecasting](https://mlsanity.com/paper/2405.05925/) (2024)
- [FuXi-2.0: Advancing machine learning weather forecasting model for practical applications](https://mlsanity.com/paper/2409.07188/) (2024)
- [Global Tropical Cyclone Intensity Forecasting with Multi-modal Multi-scale Causal Autoregressive Model](https://mlsanity.com/paper/2402.13270/) (2024)
- [Multimodal Atmospheric Super-Resolution With Deep Generative Models](https://mlsanity.com/paper/2506.22780/) (2025)
- [Improving Global Weather and Ocean Wave Forecast with Large Artificial Intelligence Models](https://mlsanity.com/paper/2401.16669/) (2024)
- [How to use score-based diffusion in earth system science: A satellite nowcasting example](https://mlsanity.com/paper/2505.10432/) (2025)
- [Efficient Generative AI Boosts Probabilistic Forecasting of Sudden Stratospheric Warmings](https://mlsanity.com/paper/2510.26376/) (2025)
- [HydroDiffusion: Diffusion-Based Probabilistic Streamflow Forecasting with a State Space Backbone](https://mlsanity.com/paper/2512.12183/) (2025)
- [Improving ensemble extreme precipitation forecasts using generative artificial intelligence](https://mlsanity.com/paper/2407.04882/) (2024)
- [YanTian: An Application Platform for AI Global Weather Forecasting Models](https://mlsanity.com/paper/2410.04539/) (2024)
- [XiChen: A global weather observation-to-forecast machine learning system via four-dimensional variational gradient-guided flexible assimilation](https://mlsanity.com/paper/2507.09202/) (2025)
- [UniExtreme: A Universal Foundation Model for Extreme Weather Forecasting](https://mlsanity.com/paper/2508.01426/) (2025)
- [A unified multimodal understanding and generation model for cross-disciplinary scientific research](https://mlsanity.com/paper/2601.01363/) (2026)
- [Quantile Regression, Variational Autoencoders, and Diffusion Models for Uncertainty Quantification: A Spatial Analysis of Sub-seasonal Wind Speed Prediction](https://mlsanity.com/paper/2510.16958/) (2025)
- [Revealing the Potential of Learnable Perturbation Ensemble Forecast Model for Tropical Cyclone Prediction](https://mlsanity.com/paper/2510.23794/) (2025)
- [Synergistic Neural Forecasting of Air Pollution with Stochastic Sampling](https://mlsanity.com/paper/2510.23977/) (2025)
- [A Synergistic Approach: Dynamics-AI Ensemble in Tropical Cyclone Forecasting](https://mlsanity.com/paper/2602.22533/) (2026)
- [3DTCR: Vortex-following generative reconstruction improves tropical cyclone intensity prediction from global AI weather forecasts](https://mlsanity.com/paper/2603.13049/) (2026)
- [Accurate Precipitation Forecast by Efficiently Learning from Massive Atmospheric Variables and Unbalanced Distribution](https://mlsanity.com/paper/2603.26108/) (2026)

## Abstract (arXiv)

Significant advancements in the development of machine learning (ML) models for weather forecasting have produced remarkable results. State-of-the-art ML-based weather forecast models, such as FuXi, have demonstrated superior statistical forecast performance in comparison to the high-resolution forecasts (HRES) of the European Centre for Medium-Range Weather Forecasts (ECMWF). However, ML models face a common challenge: as forecast lead times increase, they tend to generate increasingly smooth predictions, leading to an underestimation of the intensity of extreme weather events. To address this challenge, we developed the FuXi-Extreme model, which employs a denoising diffusion probabilistic model (DDPM) to restore finer-scale details in the surface forecast data generated by the FuXi model in 5-day forecasts. An evaluation of extreme total precipitation (textrmTP), 10-meter wind speed (textrmWS10), and 2-meter temperature (textrmT2M) illustrates the superior performance of FuXi-Extreme over both FuXi and HRES. Moreover, when evaluating tropical cyclone (TC) forecasts based on International Best Track Archive for Climate Stewardship (IBTrACS) dataset, both FuXi and FuXi-Extreme shows superior performance in TC track forecasts compared to HRES, but they show inferior performance in TC intensity forecasts in comparison to HRES.