# ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast arXiv: https://arxiv.org/abs/2402.01295 ยท HTML: https://mlsanity.com/paper/2402.01295/ ## Key facts - Cited by 19 papers in the map (data through 14 Oct 2026). - Builds on 19 of its 59 references that are in the map. - First posted on arXiv 2 Feb 2024. ## Builds on (most cited first, 19 of 19) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020) - [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021) - [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017) - [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022) - [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017) - [WeatherBench: A benchmark dataset for data-driven weather forecasting](https://mlsanity.com/paper/2002.00469/) (2020) - [Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers](https://mlsanity.com/paper/2111.13587/) (2021) - [FuXi: A cascade machine learning forecasting system for 15-day global weather forecast](https://mlsanity.com/paper/2306.12873/) (2023) - [FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators](https://mlsanity.com/paper/2208.05419/) (2022) - [FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead](https://mlsanity.com/paper/2304.02948/) (2023) - [Neural networks for post-processing ensemble weather forecasts](https://mlsanity.com/paper/1805.09091/) (2018) - [U-Net Transformer: Self and Cross Attention for Medical Image Segmentation](https://mlsanity.com/paper/2103.06104/) (2021) - [A Closer Look at the Optimization Landscapes of Generative Adversarial Networks](https://mlsanity.com/paper/1906.04848/) (2019) - [FuXi-Extreme: Improving extreme rainfall and wind forecasts with diffusion model](https://mlsanity.com/paper/2310.19822/) (2023) - [Global Extreme Heat Forecasting Using Neural Weather Models](https://mlsanity.com/paper/2205.10972/) (2022) - [Algorithmic Hallucinations of Near-Surface Winds: Statistical Downscaling with Generative Adversarial Networks to Convection-Permitting Scales](https://mlsanity.com/paper/2302.08720/) (2023) - [Kunyu: A High-Performing Global Weather Model Beyond Regression Losses](https://mlsanity.com/paper/2312.08264/) (2023) - [CMIP X-MOS: Improving Climate Models with Extreme Model Output Statistics](https://mlsanity.com/paper/2311.03370/) (2023) ## Built on by (most cited first, 19 of 19) - [A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers](https://mlsanity.com/paper/2508.21148/) (2025) - [Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling](https://mlsanity.com/paper/2405.13796/) (2024) - [CRA5: Extreme Compression of ERA5 for Portable Global Climate and Weather Research via an Efficient Variational Transformer](https://mlsanity.com/paper/2405.03376/) (2024) - [PostCast: Generalizable Postprocessing for Precipitation Nowcasting via Unsupervised Blurriness Modeling](https://mlsanity.com/paper/2410.05805/) (2024) - [High-Resolution Probabilistic Data-Driven Weather Modeling with a Stretched-Grid](https://mlsanity.com/paper/2511.23043/) (2025) - [FNP: Fourier Neural Processes for Arbitrary-Resolution Data Assimilation](https://mlsanity.com/paper/2406.01645/) (2024) - [PredBench: Benchmarking Spatio-Temporal Prediction across Diverse Disciplines](https://mlsanity.com/paper/2407.08418/) (2024) - [Benchmarking Physics-Informed Time-Series Models for Operational Global Station Weather Forecasting](https://mlsanity.com/paper/2406.14399/) (2024) - [ExEBench: Benchmarking Foundation Models on Extreme Earth Events](https://mlsanity.com/paper/2505.08529/) (2025) - [A comparison of stretched-grid and limited-area modelling for data-driven regional weather forecasting](https://mlsanity.com/paper/2507.18378/) (2025) - [Learning to Advect: A Neural Semi-Lagrangian Architecture for Weather Forecasting](https://mlsanity.com/paper/2601.21151/) (2026) - [Can AI Weather Models Predict Beyond Two Weeks? A Quantitative Benchmark and Analysis of Long Rollouts](https://mlsanity.com/paper/2605.30184/) (2026) - [Transforming Weather Data from Pixel to Latent Space](https://mlsanity.com/paper/2503.06623/) (2025) - [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) - [Comparing skill of historical rainfall data based monsoon rainfall prediction in India with NWP forecasts](https://mlsanity.com/paper/2402.07851/) (2024) - [Synergistic Neural Forecasting of Air Pollution with Stochastic Sampling](https://mlsanity.com/paper/2510.23977/) (2025) - [Generative 3D Gaussian Splatting for Arbitrary-ResolutionAtmospheric Downscaling and Forecasting](https://mlsanity.com/paper/2604.07928/) (2026) - [RealBench: Benchmarking Data-Driven Numerical Weather Forecasting Under Operational Conditions and Extreme Event Challenges](https://mlsanity.com/paper/2605.24945/) (2026) - [Neptune: An AI model for Global Ocean Subseasonal Prediction](https://mlsanity.com/paper/2609.08606/) (2026) ## Abstract (arXiv) Data-driven weather forecast based on machine learning (ML) has experienced rapid development and demonstrated superior performance in the global medium-range forecast compared to traditional physics-based dynamical models. However, most of these ML models struggle with accurately predicting extreme weather, which is related to training loss and the uncertainty of weather systems. Through mathematical analysis, we prove that the use of symmetric losses, such as the Mean Squared Error (MSE), leads to biased predictions and underestimation of extreme values. To address this issue, we introduce Exloss, a novel loss function that performs asymmetric optimization and highlights extreme values to obtain accurate extreme weather forecast. Beyond the evolution in training loss, we introduce a training-free extreme value enhancement module named ExBooster, which captures the uncertainty in prediction outcomes by employing multiple random samples, thereby increasing the hit rate of low-probability extreme events. Combined with an advanced global weather forecast model, extensive experiments show that our solution can achieve state-of-the-art performance in extreme weather prediction, while maintaining the overall forecast accuracy comparable to the top medium-range forecast models.