Graph-Learned Spring SST Evolution and Land-Ocean Synergy Improve Yangtze Summer Rainfall Prediction
Abstract
Seasonal prediction of Yangtze summer rainfall is challenged by nonstationary oceanic precursors. Here we demonstrate that accounting for the evolution of spring SST and state-dependent land surface information improves predictability. Graph total variation (GTV) is applied to March-May SST fields over the western Pacific, Niño3.4, and tropical Indian Ocean to identify spatially coherent, month-specific SST coefficients for each grid cell and month to form a scalar GTV index. The learned coefficient patterns capture April-to-May ENSO decay and an opposing Indian Ocean adjustment, consistent with coupled Indo-Pacific variability. During 2008-2022, the GTV index prediction outperforms Niño3.4-based and sparse-regression benchmarks. Further improvement is achieved by including interactions of May Indo-China Peninsula soil moisture with Indian Ocean SST, importantly also correcting several underestimated wet summers. Circulation diagnostics associate the learned index with a summer geopotential-height dipole and Yangtze moisture convergence, while dry-Indochina/warm-Indian-Ocean conditions coincide with stronger westward extension of the western Pacific subtropical high.
Related
- Constructing physical-based rainfall landslides prediction model: insights from rainfall threshold curves database of slope units
- Comparison of data-driven and numerical weather prediction models for rainfall nowcasting in major cities of Bangladesh
- Soil intrinsic penetration (SIP) index: decoupling environmental noise from mechanical resistance to improve soybean yield prediction
- Assessment of Rainfall Variability and Its Impact on Soil Erosion Risk Using Rainfall Erosivity in Kwara State, Nigeria
- Rainfall and salinity-related landscapes shape cholera persistence in Haiti
BibTeX
@misc{gao2026graphlearned,
title = {{Graph-Learned Spring SST Evolution and Land-Ocean Synergy Improve Yangtze Summer Rainfall Prediction}},
author = {Yifu Gao and Haishan Chen and Runze Li and Efi Foufoula-Georgiou and Jasper A. Vrugt},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.23094},
eprint = {2610.23094},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.23094},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Graph-Learned Spring SST Evolution and Land-Ocean Synergy Improve Yangtze Summer Rainfall Prediction
AU - Yifu Gao
AU - Haishan Chen
AU - Runze Li
AU - Efi Foufoula-Georgiou
AU - Jasper A. Vrugt
PY - 2026
DA - 2026/10/16
PB - arXiv
DO - 10.48550/arxiv.2610.23094
UR - https://arxiv.org/abs/2610.23094
AB - Seasonal prediction of Yangtze summer rainfall is challenged by nonstationary oceanic precursors. Here we demonstrate that accounting for the evolution of spring SST and state-dependent land surface information improves predictability. Graph total variation (GTV) is applied to March-May SST fields over the western Pacific, Niño3.4, and tropical Indian Ocean to identify spatially coherent, month-specific SST coefficients for each grid cell and month to form a scalar GTV index. The learned coefficient patterns capture April-to-May ENSO decay and an opposing Indian Ocean adjustment, consistent with coupled Indo-Pacific variability. During 2008-2022, the GTV index prediction outperforms Niño3.4-based and sparse-regression benchmarks. Further improvement is achieved by including interactions of May Indo-China Peninsula soil moisture with Indian Ocean SST, importantly also correcting several underestimated wet summers. Circulation diagnostics associate the learned index with a summer geopotential-height dipole and Yangtze moisture convergence, while dry-Indochina/warm-Indian-Ocean conditions coincide with stronger westward extension of the western Pacific subtropical high.
ER -