Climate variability and models

Neueste Paper

• Beyond Pointwise Error: A Multi-Metric Evaluation of Spatial Climate Downscaling

  Loys Masquelier, Etienne Le Naour · 2. Oktober 2026

  Climate downscaling aims to reconstruct fine scale spatial fields from coarse resolution inputs. Evaluating the quality of these reconstructions is challenging: low pointwise error can come at the cost of fine scale variability, while realistic spatial variability can be achieved with inaccurate loc…

• Learning Hierarchical Causal Representations of the Effects of Forcings on Temperature in Climate Models

  Shan Zhao, Ilija Trajkovic, Julia Kaltenborn, Yaniv Gurwicz, Peer Nowack, David Rolnick, Julien Boussard · 28. September 2026

  Machine learning (ML) emulators provide a fast and cost-effective method to simulate climate change scenarios after being trained on Earth System Models projections. However, the black-box nature of those data-driven approaches limit the usability and trustworthiness of their outputs and in particul…

• Understanding Perturbed Parameter Ensemble Sensitivities Using A Contrastive Learning Approach

  Da Fan, David John Gagne II, Gregory S Elsaesser, Brian Medeiros, Addisu G Semie, Qingyuan Yang, Akila Sampath, Subashree Venkatasubramanian · 28. September 2026

  Perturbed parameter ensembles (PPEs) reveal how physics parameters affect climate simulations, but interpreting parameter sensitivities across multivariate, spatially structured outputs remains challenging, particularly when calibrating models against observations. We develop an explainable contrast…

• Capturing Unseen Spatial Heat Extremes Through Dependence-Aware Generative Modeling

  Xinyue Liu, Xiao Peng, Shuyue Yan, Yuntian Chen, Dongxiao Zhang, Zhixiao Niu, Hui-Min Wang, Xiaogang He · 25. September 2026

  Observed records of climate extremes provide an incomplete view of plausible hazards, missing "unseen" events beyond historical experience. Ignoring spatial dependence further underestimates hazards striking multiple locations simultaneously. We introduce DeepX-GAN (Dependence-Enhanced Embedding for…

• Lightweight Probabilistic Downscaling from a Deterministic Base Model

  Joseph McLean, Tiffany Vlaar, Sigrid Passano Hellan, Linus Ericsson · 25. September 2026

  Climate data downscaling is the task of increasing the spatial resolution of climate data, typically by generating fine-resolution regional climate data from coarse global model output. Recent machine learning (ML) work in the related task of weather forecasting has seen significant improvements due…

• Evaluating Cross-region Generalization for Wavelet-Diffusion Precipitation Downscaling

  Weikang Qian, Yixin Wen, Chugang Yi, Zhi Li, Lingcheng Li, Haizhao Yang · 25. September 2026

  Diffusion models have shown strong potential for kilometer-scale precipitation downscaling, but their performance in geographically unseen regions and event regimes remains insufficiently understood. Building on the wavelet diffusion model (WDM) framework, this study evaluates cross-region and cross…

• Learning Prognostic Variables for AI Convective Parameterizations via Symbolic Distillation

  Jurij Sch\"onfeld, Tom Beucler, Julien Savre, Steven Sherwood, Veronika Eyring · 22. September 2026

  Hybrid AI-physics climate modeling aims to improve coarse (~100km-resolution) Earth system models by learning to parameterize subgrid processes from high-fidelity data. However, this so far mostly involves local-in-time, diagnostic parameterizations, in which the subgrid state depends only on the cu…

• Optimizing Geoengineering Interventions Using Differentiable Climate Models

  Pulkit Dubey, Dorian S. Abbot, Ashesh Chattopadhyay · 14. September 2026

  The deployment of a geoengineering program to cool Earth's climate may be imminent. It is crucial that tools be developed to ensure that such a program would achieve its objectives while minimizing disruption. Here we exploit recently developed differentiable atmospheric models to demonstrate a nove…

• Steering Diffusion Priors with Sparse Observations for High-Resolution Temperature Downscaling

  Anirudh Avireddy, Manmeet Singh, Shivanshi Singh, Ayush Raj, Saptarishi Dhanuka, Parthasarathi Mukhopadhyay, Sandeep Juneja · 10. September 2026

  Local heatwave hazard depends on fine-scale air temperature, but ground stations are sparse and reanalysis products such as ERA5 cannot resolve the terrain and land-surface contrasts that shape real heat exposure. We present a conditional diffusion emulator for high-resolution 2-m temperature downsc…

• Earth System World Model for What-If Simulations: A Case Study for Terrestrial Ecosystems

  Zhihao Wang, Ruichen Wang, Ruohan Li, Lei Ma, George Hurtt, Xiaowei Jia, Gengchen Mai, Shaowen Wang, Yiqun Xie · 9. September 2026

  Machine learning emulators have become essential for accelerating expensive Earth-system simulations, but most existing approaches remain passive forecasters: they reproduce simulator trajectories under prescribed forcings without an explicit interaction mechanism for user-specified interventions. T…

• CDEP Agent: Connecting Meteorologically Detected Temporal Compound Events to Real-World Documentary Evidence

  Zhuoran Li, Weiyi Kong, Boer Zhang · 1. September 2026

  Compound drought-to-extreme-precipitation (CDEP) events are recognized in climate science as a growing driver of extreme impact, but whether this recognition carries over into real-world early warning and post-event documentation is unknown, so a meteorologically real CDEP event may pass with neithe…

• Precipitation Downscaling Using Foundation Model-Conditioned Diffusion

  Victor Nascimento Ribeiro, Jorge Guevara, Jorge Sebastian Moraga, Chris Lucas, Natalie Lord, Andrew Taylor, Edward Lockhart, Will Trojak, Johannes Schmude, Anne Jones · 27. August 2026

  High-resolution precipitation fields are essential for hydrological impact assessment, yet global climate model outputs are too coarse and biased for direct use. AI-based statistical downscaling with diffusion models offers a promising approach, but the mechanism by which large-scale atmospheric pre…

• Unfolding the Interdisciplinary Complexities of Climate Science: Fuxi-Climate Foundational Model

  Zhengyu Shi, Shaojie Shi, Rui Xu, Bohao Lv, Zhichao Chen, Jiaran Hao, Zijian Chen, Weiqi Tang, Yuan Qi, Yinghui Xu, Libo Wu · 25. August 2026

  Climate research and decision-making require integrating evidence across physical processes, socio-economic dynamics and policy responses. Large language models (LLMs) have been explored for accessing and synthesizing climate knowledge, but their ability to support structured interdisciplinary reaso…

• SAE-Xplainers: Rule-Based Feature Interpretation for Extreme Earth Events

  Hugo Porta, Emanuele Dalsasso, Chang Xu, Theo Gnassounou, Devis Tuia · 21. August 2026

  The emergence of large-scale Weather and Climate (W&C) datasets offers new opportunities for modeling extreme Earth events (ExEE) and their impacts using deep learning. However, their adoption in operational settings remains limited by the lack of models' interpretability. While for conventional tex…

• Interpretable AI predicts a 2026 summer dry anomaly in central China

  Anran Wang, Wen Shi, Yong Luo, Jianbin Huang, Lijuan Chen, Junhu Zhao, Weixin Jin, Huihui Yuan · 20. August 2026

  Seasonal precipitation anomalies are largely regulated by atmospheric circulation, which dynamical models predict with greater reliability than precipitation itself. Here, we employ a deep learning model that translates dynamical circulation predictions into precipitation estimates. Predictions init…

• Stochastic Emulation of a Fully Coupled Preindustrial E3SMv3 Simulation

  Elynn Wu, James P. C. Duncan, Troy Arcomano, Jeremy McGibbon, Oliver Watt-Meyer, Christopher S. Bretherton, Naser Mahfouz, Claudia Tebaldi, Luke Van Roekel, Andrew Roberts, Wuyin Lin, Finn Rebassoo, Jean-Christophe Golaz, Peter M. Caldwell · 12. August 2026

  We present a stochastic coupled emulator of E3SM version 3, built on the SamudrACE framework, which couples an atmosphere emulator (ACE2) with a full-depth ocean emulator (Samudra). We replace the deterministic atmosphere emulator with its stochastic counterpart, ACE2S, and fine-tune the coupled sys…

• Deep Learning-Based Statistical Downscaling of Sea Surface Temperature Using a Residual Corrective Neural Network

  Onkar Jadhav, Tim French, Ivica Janekovic, Nicole L. Jones, Matthew Rayson · 12. August 2026

  The large-scale oceanic and atmospheric forecasts provided by global climate models typically lack sufficient resolution to accurately capture the response of the coastal ocean to atmospheric forcing and coastal circulation that drive fine-scale SST variability. Dynamical downscaling is computationa…

• Projected climate memory and inherited warm-tail risk in accelerated European summer warming

  Mauricio Herrera-Mar\'in, Alex Godoy-Fa\'undez, Diego Rivera · 12. August 2026

  European summer warming reflects interactions among background change, persistent ocean--land--circulation states, and same-season variability. We develop an empirical reduced-dynamics framework that decomposes regional summer indicators into inherited slow-state memory, its predictable component, a…

• Probabilistic Deep Learning for Drought Forecasting: Role of Internal Climate Variability

  Henri Funk, Cornelia Gruber, G\"oran Kauermann, Helmut K\"uchenhoff, Magdalena Mittermeier · 4. August 2026

  Predicting drought risk is essential for anticipating impacts on water resources, agriculture, ecosystems, and climate adaptation planning. Yet drought forecasts remain uncertain because variability can substantially alter regional precipitation and evaporative demand. Treating this variability as u…

• A Spatial Persistence Gradient in European Warming Consistent with North Atlantic Cold-Blob Influence

  Mauricio Herrera-Mar\'in, Alex Godoy-Fa\'undez, Diego Rivera · 4. August 2026

  Europe is warming faster than the global mean, yet the spatial organisation of this acceleration remains incompletely understood. Using ERA5 reanalysis for 1950--2024 across 28 IPCC AR6 European sub-regions, we identify two connected empirical results. First, the DFA1 Hurst exponent of interannual t…

• Disentangling Forced and Internal Climate Variability in Single Realizations using Dynamic Mode Decomposition with Control

  Nathan Mankovich, Andrei Gavrilov, Gustau Camps-Valls · 22. Juli 2026

  We show that a single climate realization can be decomposed into forced and internal components by treating external forcing as a dynamical driver within a linear stochastic system, an idea grounded in pullback attractor theory. In doing so, we address a central methodological challenge in climate s…

• Lightweight Wrappers for Adapting Time Series Foundation Models to Regional Drought Forecasting

  Wentao Gao, Jiuyong Li, Lin Liu, Thuc Duy Le, Jixue Liu, Yanchang Zhao, Yun Chen · 21. Juli 2026

  Large \emph{Time Series Foundation Models} (TSFMs) demonstrate strong zero-shot forecasting capabilities across diverse domains. However, their application to regional climate forecasting faces practical challenges: model weights are often proprietary, local training records are limited, and computa…

• Residual-Guided Multi-Resolution Refinement of Foundation Models: A Case Study in Drought Forecasting

  Wentao Gao, Jiuyong Li, Lin Liu, Thuc Duy Le, Jixue Liu, Yanchang Zhao, Yun Chen · 21. Juli 2026

  Regional climate prediction presents unique challenges for time series foundation models, which typically process temporal patterns through single-pass inference. Expert climatologists, in contrast, employ multi-scale temporal analysis and iterative refinement based on systematic error diagnosis. We…

• Domain-Adaptive Climate Downscaling Under Temporal Distribution Shift

  Shuochen Wang, Nishant Yadav, Auroop R. Ganguly · 8. Juli 2026

  Deep-learning-based climate downscaling aims to learn relationships from historical low-resolution (LR) and high-resolution (HR) climate data to generate HR climate projections. However, this setting faces a temporal out-of-distribution (OOD) challenge: models trained on historical data are commonly…

• When to Trust, How to Distill: Multi-Foundation Model Guidance for Lightweight, Robust Scientific Time Series Forecasting

  Rupasree Dey, Abdul Matin, Nathan Orwick, Yao Zhang, Shrideep Pallickara, Sangmi Lee Pallickara · 19. Juni 2026

  The deployment of Time-Series Foundation Models (TSFMs) in physical sciences is hindered by a critical trade-off: while these models encode rich, universal temporal dynamics, they suffer from severe distributional misalignment when applied zero-shot to specific scientific domains, and their computat…