Climate Modeling

Follow this topic Ask about this topic

Momentum

7 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 45

All topics

Longwang: Zero-Shot Global Spatiotemporal Precipitation Downscaling with a Latent Generative Prior
May 17, 2026Yue Wang, Daniele VisioniZero-Shot Learning Climate Science
1*Department of Earth and Atmospheric Sciences, Cornell University, 112 Hollister Drive, Ithaca, 14853, NY, USA.
Beyond Linear Superposition: Discovering Climate Features in AI Weather Models with KAN-SAE
May 17, 2026Minjong CheonTransformer Interpretability Climate Science
Department of Computer Science and Engineering Sejong University
Emulating the Forced Response of Climate Models with Generative Machine Learning
May 16, 2026Graham Clyne, Julia Kaltenborn, Peer Nowack +2Neural Surrogate Modeling Surrogate Modeling
1INRIA, Paris, France · 2MILA, Montreal, Canada · 3Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Generative climate downscaling enables high-resolution compound risk assessment by preserving multivariate dependencies
May 12, 2026Takuro Kutsuna, Noriko N. Ishizaki, Norihiro Oyama +1Climate Science Climate Modeling
Toyota Central R&D Labs., Inc. · National Institute for Environmental Studies
PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting
May 9, 2026Hao Wu, Fan Xu, Yuxu Lu +9Long-Term Time Series Forecasting Climate Modeling
Tsinghua University · Tencent · Shenzhen Loop Area Institute +6
Towards accurate extreme event likelihoods from diffusion model climate emulators
May 5, 2026Peter Manshausen, Noah Brenowitz, Julius Berner +2Diffusion Model Guidance Climate Science
NVIDIA, Santa Clara, CA, USA
Conditional Flow Matching for Probabilistic Downscaling of Maximum 3-day Snowfall in Alaska
Apr 28, 2026Douglas Brinkerhoff, Elizabeth FischerUncertainty Quantification Climate Science
Department of Computer Science, University of Montana · International Arctic Research Center, University of Alaska
A Differentiable Framework for Global Circulation Model Precipitation Bias Correction
Apr 24, 2026Kamlesh Sawadekar, Seth McGinnis, Peijun Li +2Climate Science Climate Modeling
Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, Pennsylvania · Computational & Information Systems Laboratory, National Center for Atmospheric Research, Boulder, Colorado 80307
climt-paraformer: Stable Emulation of Convective Parameterization using a Temporal Memory-aware Transformer
Apr 22, 2026Shuochen Wang, Nishant Yadav, Joy Merwin Monteiro +1Neural Surrogate Modeling Climate Science
Sustainability and Data Sciences Laboratory, Northeastern University · Microsoft · Indian Institute of Science Education and Research Pune +2
Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting
Apr 20, 2026Firat Ozdemir, Yun Cheng, Salman Mohebi +11Geospatial Foundation Models Climate Science
Swiss Data Science Center (SDSC), ETH Zurich & EPFL, Zurich & Lausanne, Switzerland · ETH AI Center, ETH Zurich, Zurich, Switzerland · ETH Zurich, Zurich, Switzerland +1
Global Attention with Linear Complexity for Exascale Generative Data Assimilation in Earth System Prediction
Apr 17, 2026Xiao Wang, Zezhong Zhang, Isaac Lyngaas +10Climate Science High-Performance Computing
Oak Ridge National Laboratory, Oak Ridge, TN, USA · Auburn University, Auburn, AL, USA · Florida State University, Tallahassee, FL, USA +2
High-Resolution Climate Projections Using Diffusion-Based Downscaling of a Lightweight Climate Emulator
Feb 13, 2026Haiwen Guan, Dibyajyoti Chakraborty, Moein Darman +3Climate Science Climate Modeling
1Information Sciences and Technology, The Pennsylvania State University, University Park, Pennsylvania · Department of Applied Mathematics, University of California, Santa Cruz, California · 3Allen Institute for Artificial Intelligence (AI2), Seattle, Washington +1
Symmetric Composition of Anisotropic Operators for Global Subseasonal-to-Seasonal Climate Forecasting
Feb 4, 2026Ziyu Zhou, Yuchen Fang, Weilin Ruan +4Climate Science Time Series Forecasting
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · DAMO Academy, Alibaba Group, Hangzhou, China · The Hong Kong University of Science and Technology, Hong Kong SAR, China
Climate Surrogates for Scalable Multi-Agent Reinforcement Learning: A Case Study with CICERO-SCM
Oct 9, 2025Oskar Bohn Lassen, Serio Angelo Maria Agriesti, Filipe Rodrigues +2Neural Surrogate Modeling Surrogate Modeling
Technical University of Denmark Kongens Lyngby, Denmark
ArchesClimate: Probabilistic Decadal Ensemble Generation With Flow Matching
Sep 19, 2025Graham Clyne, Guillaume Couairon, Guillaume Gastineau +2Neural Surrogate Modeling Climate Science
ARCHES, INRIA, Paris, France · UMR LOCEAN, IPSL, Sorbonne Universit´e, IRD, CNRS, MNHN, Paris, France · Department of Computer Science, University of Colorado Boulder, USA

May 17, 2026, physics.ao-phJ/K move · Enter open · S save

Longwang: Zero-Shot Global Spatiotemporal Precipitation Downscaling with a Latent Generative Prior

Yue Wang, Daniele Visioni

1*Department of Earth and Atmospheric Sciences, Cornell University, 112 Hollister Drive, Ithaca, 14853, NY, USA.

Zero-Shot Learning Climate Science Precipitation Downscaling Climate Modeling Conditional Generative Modeling Climate Downscaling

Open paper AskPDF arXiv

High-resolution precipitation information is essential for climate impact assessment, yet global climate models remain too coarse to resolve key small-scale processes. Existing machine learning downscaling methods often require paired low- and high-resolution data for supervised learning, are tied to fixed regions or scale factors during inference, and can be computationally expensive to train and run in physical space. Here we introduce Longwang, a zero-shot latent generative framework for global spatiotemporal precipitation downscaling. Longwang learns a context-conditioned latent generative prior and combines it with a physically informed observation operator through posterior sampling, enabling daily O(10 km) precipitation fields to be generated from monthly O(100 km) inputs. On ERA5 reanalysis, Longwang outperforms standard posterior sampling with an unconditional generative prior in reconstructing fine-scale spatial patterns, preserving temporal coherence, and recovering extreme precipitation intensities. The framework further generalizes to historical climate simulations and future climate projections under substantial distribution shift.

Momentum

Related topics

Latest papers 45