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ENSO shapes salinity regimes and fish migration in the China Seas

Nature Climate Change (2026)Cite this article

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Abstract

Salinity shapes ocean circulation and marine biogeography, yet its long-term spatiotemporal variability and ecological impacts in marginal seas remain poorly constrained. We reconstruct a high-resolution sea surface salinity dataset (2000–2020) for the China Seas using a machine learning framework that integrates in situ cruises and buoys with satellite observations and diagnose drivers with an eigen microstates approach. The El Niño/Southern Oscillation (ENSO) is the dominant control, modulating evaporation–precipitation, river discharge and Kuroshio intrusion. During El Niño, sea surface salinity increases by up to 25% in ocean-dominated regions but decreases up to 21% in river-dominated zones, amplifying meridional salinity contrasts. Species-distribution models indicate a southward habitat shift up to 2.5° latitude for 90% of key fish species. Under projected ENSO intensification, salinity inhomogeneity and associated ecological impacts are likely to strengthen. These results support an ‘ENSO forcing–salinity–fishery’ positive feedback framework and call for integrating salinity dynamics into adaptive, climate-informed fisheries management.

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Fig. 1: Spatiotemporal variability of SSS in the China Seas.
Fig. 2: The eigen microstates approach reveals dominant drivers of SSS variability in the China Seas.
Fig. 3: Spatial anomalies associated with El Niño events during 2000–2020 and projected increases in the Niño 3.4 index under future emission scenarios by various CMIP6 models.
Fig. 4: Causality pathways of ENSO impacts on SSS.
Fig. 5: Zonal-mean SSS anomalies highlight distinctive dipoles during El Niño events relative to the climatological baselines.
Fig. 6: Southward migration of fish species and changes in species richness in response to SSS variations during El Niño years in the China Seas.

Data availability

The data supporting the findings of this study are available via Science Data Bank at https://www.scidb.cn/en/s/fia6Jv(ref. 62). In situ observational salinity data source can be found in Supplementary Information Section 1. The ocean current data in and around the China Seas are available at https://data.marine.copernicus.eu/. The geopotential height data, 10-m wind speed data and precipitation data are available at https://www.psl.noaa.gov/data/gridded/data.ncep.reanalysis2.html. The river discharge data are available at https://ewds.climate.copernicus.eu/datasets/cems-glofas-historical?tab=download. The Niño 3.4 index data are available at https://psl.noaa.gov/data/correlation/nina34.anom.data. China Fisheries Yearbooks can be downloaded from https://www.zgtjnj.org/. The CMIP6 model data are available at https://esgf-node.llnl.gov/projects/cmip6. FAO data can be found at https://data.fao.org/. Source data are provided with this paper.

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Acknowledgements

We thank L. Guo, Y. Li, Y. Xu, L. Wang, T. Huang, Y. Xu, C. Du, Q. Li and B. Chen for their assistance in sampling and/or analyses. This study was funded by the National Natural Science Foundation of China (grant no. 42188102 to M.D., grant no. 42141001 to X.G., G.W. and Z.W., grant no. 42450183 to J.F., grant no. 12275020 to J.F., grant no. 12135003 to X.C., grant no. 12205025 to J.F. and grant no. 42461144209 to J.F. and X.C.) and partially supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (grant no. AoE/P-601/23-N to M.D., Z.W. and G.W.) and the Cooperation Project of Zhangzhou Meteorological Bureau (grant no. ZL202402 to Z.W.). J.F. acknowledges support from the Fundamental Research Funds for the Central Universities. X.C. acknowledges support from the National Key R&D Program of China (grant no. 2023YFE0109000).

Author information

Author notes

  1. These authors contributed equally: Zhixuan Wang, Han Huang.

  2. These authors jointly supervised this work: Guizhi Wang, Minhan Dai.

Authors and Affiliations

  1. State Key Laboratory of Marine Environmental Science & College of Ocean and Earth Sciences, Xiamen University, Xiamen, China

    Zhixuan Wang, Guizhi Wang, Yue Liu, Xianghui Guo, Jianyu Hu, Ling Cao & Minhan Dai

  2. Department of Ocean Science and Department of Mathematics, Hong Kong University of Science and Technology, Hong Kong, China

    Zhixuan Wang & Jianping Gan

  3. School of Systems Science / Institute of Nonequilibrium Systems, Beijing Normal University, Beijing, China

    Han Huang, Jingfang Fan & Xiaosong Chen

  4. Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Xiamen University, Xiamen, China

    Guizhi Wang

  5. Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, CA, USA

    Tangdong Qu

  6. Carbon Neutral Innovation Research Center, Xiamen University, Xiamen, China

    Shiyun Lei

  7. Potsdam Institute for Climate Impact Research, Potsdam, Germany

    Jingfang Fan

  8. Institute for Advanced Study in Physics and School of Physics, Zhejiang University, Hangzhou, China

    Xiaosong Chen

  9. Fujian Ocean Innovation Center, Xiamen, China

    Minhan Dai

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Contributions

All authors contributed to writing and revision of the draft paper. Conceptualization: G.W. and M.D. Data collection: Z.W., H.H., Y.L., X.G. and J.H. Data analysis: Z.W., H.H., T.Q., Y.L., S.L., J.F., J.G., L.C. and X.C. Writing, reviewing and editing: Z.W., G.W. and M.D.

Corresponding authors

Correspondence to Guizhi Wang or Minhan Dai.

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Nature Climate Change thanks Kui Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Table 1 The Pearson correlation coefficients and p values for variables in Fig. 2
Full size table

Extended Data Fig. 1 Causality of the pathways linking ENSO, SSS variability, and annual change in north-to-south fishery catch differences (ACCD) revealed by Convergent Cross Mapping.

a–c, Causality of the pathway from ENSO to evaporation minus precipitation (E–P) flux to the E–P-dominated SSS mode (the first mode) to ACCD. d–f, Causality of the pathway from ENSO to river discharge to the river-dominated SSS mode (the second mode) to ACCD. g–i, Causality of the pathway from ENSO to Kuroshio intrusion (KCS/KCE) to the ocean-dominated SSS mode (the third mode) to ACCD. ρ is the skill score of Convergent Cross Mapping, which convergence indicates causation, and L is the library size. ENSO is represented by the Niño 3.4 index. In all pathways, ρ increases monotonically and converges significantly (P < 0.05 based on a two-sided bootstrap test with 50,000 resamples), confirming a coherent and statistically robust causal chain linking ENSO forcing, SSS variability, and fishery catch dynamics.

Source data

Extended Data Fig. 2 Relationship between the Nino 3.4 index and annual change in north-to-south fishery catch differences (ACCD).

R2 = 0.32, n = 21, and P = 0.007 based on a two-sided t-test.

Source data

Extended Data Fig. 3 Causation pathways between ENSO, SSS, and total fishery catch based on Convergent Cross Mapping.

a, The causality pathway from ENSO to SSS. b, The causality pathway from SSS to total fishery catch. ENSO is represented by the Niño 3.4 index. In a, b, ρ increases monotonically and converges significantly (P < 0.05 based on a two-sided bootstrap test with 50,000 resamples), confirming a coherent and statistically robust causal chain linking ENSO forcing, SSS variability and total fishery catch change.

Source data

Extended Data Fig. 4 Habitat-suitability anomalies for 31 species during El Niño events in the China Seas based on the Species Distribution Model.

Dots stand for the suitable areas under climatological conditions (habitat suitability > 0.5).

Source data

Supplementary information

Supplementary Information

Supplementary Text, Tables 1–10 and Figs. 1–15.

Source data

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Source Data Extended Data Fig. 1

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Source Data Extended Data Table 1

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Wang, Z., Huang, H., Wang, G. et al. ENSO shapes salinity regimes and fish migration in the China Seas. Nat. Clim. Chang. (2026). https://doi.org/10.1038/s41558-026-02559-3

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