Preprints
https://doi.org/10.5194/essd-2026-176
https://doi.org/10.5194/essd-2026-176
12 Mar 2026
 | 12 Mar 2026
Status: this preprint is currently under review for the journal ESSD.

Global natural wetland methane emissions (2000–2025)

Mengze Li, Robert B. Jackson, Marielle Saunois, Philippe Ciais, Ben Poulter, Josep G. Canadell, Prabir K. Patra, Hanqin Tian, Zhen Zhang, Etienne Fluet-Chouinard, Zutao Ouyang, Ting Zhang, David Beerling, Dmitry Belikov, Philippe Bousquet, Danilo Custodio, Naveen Chandra, Xinyu Dou, Nicola Gedney, Peter O. Hopcroft, Alison Hoyt, Kazuhito Ichii, Akihito Ito, Atul K. Jain, Katherine Jensen, Fortunat Joos, Thomas Kleinen, Masayuki Kondo, Fa Li, Tingting Li, Xiangyu Liu, Shamil Maksyutov, Avni Malhotra, Adrien Martinez, Kyle McDonald, Joe R. Melton, Paul Miller, Jurek Müller, Yosuke Niwa, Shufen Pan, Shushi Peng, Changhui Peng, Zhangcai Qin, Peter Raymond, William Riley, Arjo Segers, Rona L. Thompson, Aki Tsuruta, Xi Yi, Kunxiaojia Yuan, Wenxin Zhang, Bo Zheng, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang

Abstract. Wetlands are the largest natural source of atmospheric methane (CH4), yet comprehensive global budgets are typically delayed by several years, preventing a timely understanding of CH4 sources, sinks, and their trends. To reduce this delay, we present a model emulator-driven framework and accompanying workflow that enable timely, continuous emission updates and applying the framework to a global dataset of natural vegetated wetland CH4 emissions to extend the most recent Global Methane Budget (GMB; Saunois et al., 2025) record through 2025 at monthly 1°x1° resolution. We developed a machine-learning emulator to reconstruct spatially explicit monthly emission fields (global R2 =0.65 ± 0.003 (mean ± 95 % CI, hereafter) and RMSE=5.49 ± 0.12 ×10-3 Tg CH4/year in test data which is ~30 % of the total data). The emulator is trained on 35 GMB model estimates (22 process-based model estimates and 13 atmospheric inversion estimates) paired with 10 ensemble realizations of 11 gridded climate predictor variables from atmospheric reanalyses. While the global mean predicted wetland CH4 emissions for 2021–2025 (157.83 ± 2.38 Tg CH4/year) are only marginally higher (~0.05 Tg CH4/year) than the 2000–2020 baseline, this stability masks a significant hemispheric redistribution of emissions. We detect a surge in Northern Hemisphere emissions in 2021–2025, with mid- and high-latitudes increasing by 0.76 ± 0.07 (z-score: 2.21) and 0.35 ± 0.03 Tg/year (z-score:1.01), respectively, while the tropics and Southern Hemisphere extratropics show offsetting negative trends (-0.95 ± 0.19 and -0.11 ± 0.02 Tg/year with z-scores of -2.81 and -0.34, respectively). The predicted emissions capture the low emissions in 2023 in South America linked to El Niño-related drought, as reported by recent studies (Ciais et al., 2026; Quinn et al., 2025). Post-2020 growth rates of emission anomalies are a magnitude higher than that in 2000–2025, suggesting an intensification of emission variability. Furthermore, we identify a distinct seasonal amplification of global emission growth peaking in late boreal summer. This new dataset and operational framework bridge the gap between latest updated budgets and low-latency monitoring, providing a scalable capacity to frequently update global emission estimates and critical early warnings of regional wetland feedback loops. The data are publicly available at https://doi.org/10.5281/zenodo.18870108 (Li et al., 2026).

Competing interests: At least one of the (co-)authors is a member of the editorial board of Earth System Science Data.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
Share
Mengze Li, Robert B. Jackson, Marielle Saunois, Philippe Ciais, Ben Poulter, Josep G. Canadell, Prabir K. Patra, Hanqin Tian, Zhen Zhang, Etienne Fluet-Chouinard, Zutao Ouyang, Ting Zhang, David Beerling, Dmitry Belikov, Philippe Bousquet, Danilo Custodio, Naveen Chandra, Xinyu Dou, Nicola Gedney, Peter O. Hopcroft, Alison Hoyt, Kazuhito Ichii, Akihito Ito, Atul K. Jain, Katherine Jensen, Fortunat Joos, Thomas Kleinen, Masayuki Kondo, Fa Li, Tingting Li, Xiangyu Liu, Shamil Maksyutov, Avni Malhotra, Adrien Martinez, Kyle McDonald, Joe R. Melton, Paul Miller, Jurek Müller, Yosuke Niwa, Shufen Pan, Shushi Peng, Changhui Peng, Zhangcai Qin, Peter Raymond, William Riley, Arjo Segers, Rona L. Thompson, Aki Tsuruta, Xi Yi, Kunxiaojia Yuan, Wenxin Zhang, Bo Zheng, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang

Status: open (until 18 Apr 2026)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Mengze Li, Robert B. Jackson, Marielle Saunois, Philippe Ciais, Ben Poulter, Josep G. Canadell, Prabir K. Patra, Hanqin Tian, Zhen Zhang, Etienne Fluet-Chouinard, Zutao Ouyang, Ting Zhang, David Beerling, Dmitry Belikov, Philippe Bousquet, Danilo Custodio, Naveen Chandra, Xinyu Dou, Nicola Gedney, Peter O. Hopcroft, Alison Hoyt, Kazuhito Ichii, Akihito Ito, Atul K. Jain, Katherine Jensen, Fortunat Joos, Thomas Kleinen, Masayuki Kondo, Fa Li, Tingting Li, Xiangyu Liu, Shamil Maksyutov, Avni Malhotra, Adrien Martinez, Kyle McDonald, Joe R. Melton, Paul Miller, Jurek Müller, Yosuke Niwa, Shufen Pan, Shushi Peng, Changhui Peng, Zhangcai Qin, Peter Raymond, William Riley, Arjo Segers, Rona L. Thompson, Aki Tsuruta, Xi Yi, Kunxiaojia Yuan, Wenxin Zhang, Bo Zheng, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang

Data sets

ERA5 monthly averaged data on single levels from 1940 to present Copernicus Climate Change Service https://doi.org/10.24381/cds.f17050d7

Global natural wetland methane emissions (2000-2025) M. Li et al. https://doi.org/10.5281/zenodo.18870108

Mengze Li, Robert B. Jackson, Marielle Saunois, Philippe Ciais, Ben Poulter, Josep G. Canadell, Prabir K. Patra, Hanqin Tian, Zhen Zhang, Etienne Fluet-Chouinard, Zutao Ouyang, Ting Zhang, David Beerling, Dmitry Belikov, Philippe Bousquet, Danilo Custodio, Naveen Chandra, Xinyu Dou, Nicola Gedney, Peter O. Hopcroft, Alison Hoyt, Kazuhito Ichii, Akihito Ito, Atul K. Jain, Katherine Jensen, Fortunat Joos, Thomas Kleinen, Masayuki Kondo, Fa Li, Tingting Li, Xiangyu Liu, Shamil Maksyutov, Avni Malhotra, Adrien Martinez, Kyle McDonald, Joe R. Melton, Paul Miller, Jurek Müller, Yosuke Niwa, Shufen Pan, Shushi Peng, Changhui Peng, Zhangcai Qin, Peter Raymond, William Riley, Arjo Segers, Rona L. Thompson, Aki Tsuruta, Xi Yi, Kunxiaojia Yuan, Wenxin Zhang, Bo Zheng, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Metrics will be available soon.
Latest update: 12 Mar 2026
Download
Short summary
We proposed a framework that combines artificial intelligence and climate data to predict natural wetland methane emissions for 2000–2025. We found that although total global emissions remained stable, Northern Hemisphere emissions surged whilst tropical emissions fell. This approach allows us to rapidly monitor emissions and provides early warnings for climate impacts.
Share
Altmetrics