Climate change is amplifying extreme weather and climate events worldwide [1]. Anthropogenic greenhouse gas emissions have disrupted the Earth's climate system, driving more frequent and severe heatwaves [2], cold spells [3], heavy precipitation [4], agricultural droughts [5], and tropical cyclones (TCs) [6]. Between 2016 and 2024, daily land temperature records show that extreme heat events occurred over four times more often than expected, while cold records declined by half [7]. These unprecedented shifts threaten human health [8, 9], infrastructure [10, 11], food security [12], biodiversity [13], and global economies [14, 15]. Therefore, reliable climate projections are essential for effective mitigation and adaptation strategies [16-18]. The Coupled Model Intercomparison Project (CMIP) [19] provides a foundation for global climate projections. Since its launch in 1995, CMIP has coordinated systematic evaluation of coupled general circulation models (GCMs). CMIP5 introduced Representative Concentration Pathways (RCPs), while CMIP6 extended this framework by incorporating Shared Socioeconomic Pathways (SSPs) through ScenarioMIP, enabling consistent simulations of emissions and socioeconomic trajectories to 2100 and facilitating integrated assessment of climate risks [20]. These advances have greatly enhanced the scientific and policy relevance of climate projections.

Our planet is facing increasingly frequent extreme events, which pose major risks to human lives and ecosystems. Recent advances in machine learning (ML), especially with foundation models (FMs) trained on extensive datasets, excel in extracting features and show promise in disaster management. Nevertheless, these models often inherit biases from training data, challenging their performance over extreme values. To explore the reliability of FM in the context of extreme events, we introduce \textbf{ExE}Bench (\textbf{Ex}treme \textbf{E}arth Benchmark), a collection of seven extreme event categories across floods, wildfires, storms, tropical cyclones, extreme precipitation, heatwaves, and cold waves. The dataset features global coverage, varying data volumes, and diverse data sources with different spatial, temporal, and spectral characteristics. To broaden the real-world impact of FMs, we include multiple challenging ML tasks that are closely aligned with operational needs in extreme events detection, monitoring, and forecasting. ExEBench aims to (1) assess FM generalizability across diverse, high-impact tasks and domains, (2) promote the development of novel ML methods that benefit disaster management, and (3) offer a platform for analyzing the interactions and cascading effects of extreme events to advance our understanding of Earth system, especially under the climate change expected in the decades to come. The dataset and code are public https://github.com/zhaoshan2/EarthExtreme-Bench.

Climate models struggle to accurately simulate precipitation, particularly extremes and the diurnal cycle. Here, we present a hybrid model that is trained directly on satellite-based precipitation observations. Our model runs at 2.8$^\circ$ resolution and is built on the differentiable NeuralGCM framework. The model demonstrates significant improvements over existing general circulation models, the ERA5 reanalysis, and a global cloud-resolving model in simulating precipitation. Our approach yields reduced biases, a more realistic precipitation distribution, improved representation of extremes, and a more accurate diurnal cycle. Furthermore, it outperforms the mid-range precipitation forecast of the ECMWF ensemble. This advance paves the way for more reliable simulations of current climate and demonstrates how training on observations can be used to directly improve GCMs.

Most state-of-the-art AI applications in atmospheric science are based on classic deep learning approaches. However, such approaches cannot automatically integrate multiple complicated procedures to construct an intelligent agent, since each functionality is enabled by a separate model learned from independent climate datasets. The emergence of foundation models, especially multimodal foundation models, with their ability to process heterogeneous input data and execute complex tasks, offers a substantial opportunity to overcome this challenge. In this report, we want to explore a central question - how the state-of-the-art foundation model, i.e., GPT-4o, performs various atmospheric scientific tasks. Toward this end, we conduct a case study by categorizing the tasks into four main classes, including climate data processing, physical diagnosis, forecast and prediction, and adaptation and mitigation. For each task, we comprehensively evaluate the GPT-4o's performance along with a concrete discussion. We hope that this report may shed new light on future AI applications and research in atmospheric science.

Nowcasting leverages real-time atmospheric conditions to forecast weather over short periods. State-of-the-art models, including PySTEPS, encounter difficulties in accurately forecasting extreme weather events because of their unpredictable distribution patterns. In this study, we design a physics-informed neural network to perform precipitation nowcasting using the precipitation and meteorological data from the Royal Netherlands Meteorological Institute (KNMI). This model draws inspiration from the novel Physics-Informed Discriminator GAN (PID-GAN) formulation, directly integrating physics-based supervision within the adversarial learning framework. The proposed model adopts a GAN structure, featuring a Vector Quantization Generative Adversarial Network (VQ-GAN) and a Transformer as the generator, with a temporal discriminator serving as the discriminator. Our findings demonstrate that the PID-GAN model outperforms numerical and SOTA deep generative models in terms of precipitation nowcasting downstream metrics.

Precipitation nowcasting based on radar data plays a crucial role in extreme weather prediction and has broad implications for disaster management. Despite progresses have been made based on deep learning, two key challenges of precipitation nowcasting are not well-solved: (i) the modeling of complex precipitation system evolutions with different scales, and (ii) accurate forecasts for extreme precipitation. In this work, we propose CasCast, a cascaded framework composed of a deterministic and a probabilistic part to decouple the predictions for mesoscale precipitation distributions and small-scale patterns. Then, we explore training the cascaded framework at the high resolution and conducting the probabilistic modeling in a low dimensional latent space with a frame-wise-guided diffusion transformer for enhancing the optimization of extreme events while reducing computational costs. Extensive experiments on three benchmark radar precipitation datasets show that CasCast achieves competitive performance. Especially, CasCast significantly surpasses the baseline (up to +91.8%) for regional extreme-precipitation nowcasting.

In recent years, data-driven modeling approaches have gained considerable traction in various meteorological applications, particularly in the realm of weather forecasting. However, these approaches often encounter challenges when dealing with extreme weather conditions. In light of this, we propose GA-SmaAt-GNet, a novel generative adversarial architecture that makes use of two methodologies aimed at enhancing the performance of deep learning models for extreme precipitation nowcasting. Firstly, it uses a novel SmaAt-GNet built upon the successful SmaAt-UNet architecture as generator. This network incorporates precipitation masks (binarized precipitation maps) as an additional data source, leveraging valuable information for improved predictions. Additionally, GA-SmaAt-GNet utilizes an attention-augmented discriminator inspired by the well-established Pix2Pix architecture. Furthermore, we assess the performance of GA-SmaAt-GNet using real-life precipitation dataset from the Netherlands. Our experimental results reveal a notable improvement in both overall performance and for extreme precipitation events. Furthermore, we conduct uncertainty analysis on the proposed GA-SmaAt-GNet model as well as on the precipitation dataset, providing additional insights into the predictive capabilities of the model. Finally, we offer further insights into the predictions of our proposed model using Grad-CAM. This visual explanation technique generates activation heatmaps, illustrating areas of the input that are more activated for various parts of the network.

"We know that flooding has been getting worse," said study lead author Frances Davenport, a PhD student in Earth system science in Stanford's School of Earth, Energy & Environmental Sciences (Stanford Earth). "Our goal was to understand why extreme precipitation is increasing, which in turn could lead to better predictions about future flooding." Among other impacts, global warming is expected to drive heavier rain and snowfall by creating a warmer atmosphere that can hold more moisture. Scientists hypothesize that climate change may affect precipitation in other ways, too, such as changing when and where storms occur. Revealing these impacts has remained difficult, however, in part because global climate models do not necessarily have the spatial resolution to model these regional extreme events.

From lake-draining drought in California to bridge-breaking floods in China, extreme weather is wreaking havoc. Preparing for weather extremes in a changing climate remains a challenge, however, because their causes are complex and their response to global warming is often not well understood. Now, Stanford researchers have developed a machine learning tool to identify conditions for extreme precipitation events in the Midwest, which account for over half of all major U.S. flood disasters. Published in Geophysical Research Letters, their approach is one of the first examples using AI to analyze causes of long-term changes in extreme events and could help make projections of such events more accurate. "We know that flooding has been getting worse," said study lead author Frances Davenport, a Ph.D. student in Earth system science in Stanford's School of Earth, Energy & Environmental Sciences (Stanford Earth).