Generalizable Neural Downscaling of Earth System Model Wind Fields via Continuous Dynamics Modeling
Abstract
Accurate high-resolution wind field simulations are critical for resolving fine-scale atmospheric dynamics, yet the simulation of wind fields in Earth System Models (ESMs) remains limited by coarse spatial resolution and systematic biases. To address this, data-driven down scaling techniques have been widely used to enhance coarse-resolution ESM outputs. However, existing methods are typically tied to fixed discretizations, limiting generalization across models with different native resolutions. Here we formulate global near-surface wind downscaling as an operator-learning problem on continuous atmospheric state fields and develop a downscaling neural operator that maps coarse-scale fields to fine-scale counterparts across heterogeneous discretizations. The operator learning-based neural downscaling framework outperforms dominant baselines, recovers fine-scale physical structures, and generalizes to previously unseen ESMs and future climate scenarios without retraining, while preserving long-term wind projection trends. These findings establish a generalizable paradigm for high-resolution climate downscaling across diverse simulation outputs and future scenarios.
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BibTeX
@misc{yu2026generalizable,
title = {{Generalizable Neural Downscaling of Earth System Model Wind Fields via Continuous Dynamics Modeling}},
author = {Chenxi Yu and Jianan Wei and Hanlin Kong and Hao Sun and Bian He and Wenguan Wang},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.03757},
eprint = {2610.03757},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.03757},
}
RIS
TY - UNPB
TI - Generalizable Neural Downscaling of Earth System Model Wind Fields via Continuous Dynamics Modeling
AU - Chenxi Yu
AU - Jianan Wei
AU - Hanlin Kong
AU - Hao Sun
AU - Bian He
AU - Wenguan Wang
PY - 2026
DA - 2026/10/01
PB - arXiv
DO - 10.48550/arxiv.2610.03757
UR - https://arxiv.org/abs/2610.03757
AB - Accurate high-resolution wind field simulations are critical for resolving fine-scale atmospheric dynamics, yet the simulation of wind fields in Earth System Models (ESMs) remains limited by coarse spatial resolution and systematic biases. To address this, data-driven down scaling techniques have been widely used to enhance coarse-resolution ESM outputs. However, existing methods are typically tied to fixed discretizations, limiting generalization across models with different native resolutions. Here we formulate global near-surface wind downscaling as an operator-learning problem on continuous atmospheric state fields and develop a downscaling neural operator that maps coarse-scale fields to fine-scale counterparts across heterogeneous discretizations. The operator learning-based neural downscaling framework outperforms dominant baselines, recovers fine-scale physical structures, and generalizes to previously unseen ESMs and future climate scenarios without retraining, while preserving long-term wind projection trends. These findings establish a generalizable paradigm for high-resolution climate downscaling across diverse simulation outputs and future scenarios.
ER -