We quantified spatial patterns, regional aggregates and temporal emission trends in reconstructed wetland CH₄ emissions for 2000–2025, and compared them with corresponding GMB estimates over 2000–2020. We performed analyses for 18 geographic regions and for five latitude bands.

We analyzed recent emission changes by comparing 2021–2025 against 2000–2020 baseline at both grid-cell and regional scales (Figs. 3, 4). For Fig. 3, gridded monthly emissions were aggregated to per-grid mean emissions for each period (2021–2025 and 2000–2020). Emissions presented in Fig. 4 were summed across grid cells within each region. Emission changes between the two periods were calculated as paired differences for each grid cell and model–ensemble pair. Therefore, the reported mean and 95 % confidence interval for emission changes reflect the uncertainty of the paired differences themselves. We examined interannual variability and long-term annual emission changes by aggregating monthly emissions to annual totals for both the latitude bands and 18 geographic regions (Figs. 5, S6, S8). Ordinary least squares regression was applied to monthly emissions over 2000–2025 to estimate emission trends (Fig. 6).

Emulator skill uncertainty was evaluated using held-out test period R2, RMSE, NRMSE and prediction residuals relative to the GMB fields (Sects. 2.2.3 and 3.1). Uncertainty in reconstructed wetland CH₄ emissions was estimated from the ensemble spread across all reconstructions generated from the 35 GMB estimates and 10 ERA5 ensemble members. This propagated two sources of uncertainty through the framework: (i) spread in the underlying GMB estimates and (ii) uncertainty in the predictors represented by the ERA5 ensemble. We use 95 % confidence interval (CI) to represent this emission uncertainty.

We begin by evaluating model performance at the spatial scales most relevant to CH₄ budgeting. Aggregated annual wetland CH₄ emissions at the global and latitudinal-band scales show that the emulator reproduces the overall magnitude, temporal variability, and broad zonal structure of the GMB estimates over 2000–2020 (Fig. S6), providing a large-scale context for the finer grid-cell evaluation presented below.

Across the two test windows (2000–2002 and 2018–2020), the XGBoost model reproduces monthly wetland CH₄ emissions well. Globally, the coefficient of determination was R2 of 0.65 ± 0.003 (mean ± 95 % CI, hereafter; Fig. 1a), which refers to the mean grid-cell R2 across all global land cells over the two test periods, with the 95 % confidence interval calculated across grid cells. This reported reconstructive skill indicates strong predictability from predictor variables. However, the model performance varies geographically. Mean R2 is higher in the Southern Hemisphere (SH) than in the Northern Hemisphere (NH) (0.77 ± 0.006 compared with 0.62 ± 0.004, respectively), and higher in the tropics than in the extratropics (0.78±0.004 and 0.58 ± 0.004, respectively). In the top emitting regions, mean R2 values are generally higher than in regions with lower emissions (Fig. S1a). Global RMSE over test periods is 5.49±0.12×10-3 TgCH₄ yr⁻¹ (Fig. 1b). RMSE for the tropics is about twice that of the RMSE for the extratropics (9.37 ± 0.27 × 10⁻³ and 3.35 ± 0.08 × 10⁻³ Tg CH₄ yr⁻¹, respectively). Top emitting regions exhibit higher RMSE, e.g., RMSE doubled for top 10 % emitting regions compared with top 10 %–20 % emitting regions (Fig. S1b). Regionally, mean R2 values are highest in Southeast Asia and Korea-Japan (>0.8), and lowest in Russia and Canada (∼ 0.5) (Fig. S2a). Mean RMSE is highest in the Americas (Northern South America and Central America), and lowest in the Middle East and Central Asia. Generally, NRMSE is inversely correlated to R2, indicating a better performance of our model in top emitting regions (Fig. 1c). Temporal variation of model performance reveals the lowest R2 in January/February in NH high latitudes (Fig. S3), and the highest RMSE in July–September in the tropics (Fig. S4). We further evaluate how these spatial and temporal differences in model skill influence long-term variability in annual emissions in Sect. 3.3.

To examine predictive uncertainty, we compare wetland CH₄ emissions from the GMB models (CH₄_GMB; Fig. 2a) with XGBoost predictions (CH₄_pred; Fig. 2b) over the two test periods. Globally, CH₄_pred reproduces both the magnitude and spatial pattern of CH₄_GMB, including the major hotspots concentrated in the tropics, such as Amazon Basin (central-western Amazon floodplains, Pantanal and Moxos plains), Congo Basin, the Sudd wetlands in South Sudan, and Southeast Asia (Sumatra, Borneo, Peninsular Malaysia, and New Guinea), with additional hotspots in NH high latitudes, such as West Siberian Plain and Hudson/James Bay Lowlands.

The differences in emissions (Fig. 2c) and the corresponding percent emission differences (Fig. 2d) between CH₄_GMB and CH₄_pred highlight spatial biases. During the test periods, the global mean predicted wetland CH₄ emission is 158.5±2.1 Tg CH₄ yr⁻¹, which is 2.3 Tg CH₄ yr⁻¹ lower than the global mean of CH₄_GMB (160.7 ± 2.2 Tg CH₄ yr⁻¹), corresponding to a 1.4 % underestimation (Fig. 2c). This difference is within the estimated uncertainty range of the two global means. Underestimation is strongest over high-emission systems, such as the central-western Amazon Basin, Southeast Asia, Congo Basin and West Siberian Plain (Fig. 2c), where our model predicted emissions are lower than GMB estimates, but percent differences are smaller because the denominator (CH₄_GMB) is large. For example, in Southeast Asia, the mean bias is -0.44 TgCH₄ yr⁻¹, yet the relative bias is only -1.7 % given a high regional mean emission (CH₄_GMB of 25.2 Tg CH₄ yr⁻¹). Monthly anomalies in global emissions reveal small disagreements between the predictions and GMB estimates (∼±4 Tg CH₄ yr⁻¹) (Fig. S5). Largest discrepancies occur in July/August 2020 (underestimation of 15 Tg CH₄ yr⁻¹).

Figure 3 shows spatial maps of global predicted mean wetland CH₄ emissions for 2021–2025 (Fig. 3a) and emission differences between 2021–2025 and 2000–2020. The 2021–2025 mean emission is 157.8 ± 2.4 TgCH₄ yr⁻¹ (z=3.72, with the z-score indicating how far each region's mean emission estimate deviates from the cross-region mean, measured in standard deviation units), representing a non-significant increase of 0.05 Tg CH₄ yr⁻¹ relative to the 2000–2020 mean. However, changes are more pronounced across latitudes. NH mid- and high-latitude wetland emissions increase during 2021–2025 by 0.76 ± 0.07 (from 29.9 ± 1.1 in 2000–2020 to 30.7 ± 1.2 in 2021–2025, z=2.21) and 0.35 ± 0.03 (from 11.0 ± 0.7 to 11.4±0.7 TgCH₄ yr⁻¹, z=1.01), respectively, compared with 2000–2020 emissions. These changes correspond to ∼ 2.5 % and ∼ 3.2 % increase. The continued growth in these regions in 2021–2025 (relative to 2000–2020), together with increases reported for 2010–2018 relative to 1981–1989 (Feron et al., 2024), suggests an increasing role of boreal and temperate wetlands in recent decades of global warming. In contrast, emissions decrease in the tropics and SH extratropics during 2021–2025. The tropics exhibit a 0.95 ± 0.19 TgCH₄ yr⁻¹ emission decrease (z=-2.81), from 113.9 ± 2.5 (2000–2020) to 112.9 ± 2.5 TgCH₄ yr⁻¹ (2021–2025), while SH extratropics emissions decline by 3.5 % (-0.11 ± 0.02 TgCH₄ yr⁻¹, z=-0.34) from 3.1 ± 0.1 (2000–2020) to 3.0 ± 0.1 TgCH₄ yr⁻¹ (2021–2025).

We further assess regional wetland CH₄ emission changes in 2021–2025 compared with 2000–2020 across 18 regions using XGBoost predictions (Fig. 4). Four regions show no significant change (p≥0.05 from two-sided paired significance test, labelled “ns”). Among the 14 regions with significant changes, nine regions show increases and five show decreases (Northern South America, Brazil, Southwest South America, Northern Africa, and Equatorial Africa). Notably, all regions with decreases are located in South America and Africa. The largest emission changes (2021–2025 relative to 2000–2020) occur in Brazil (-0.59 ± 0.09 TgCH₄ yr⁻¹), Canada (+0.46 ± 0.05 TgCH₄ yr⁻¹), Southwest South America (-0.43 ± 0.07 TgCH₄ yr⁻¹), Russia (+0.31 ± 0.03 TgCH₄ yr⁻¹), Southeast Asia (+0.22 ± 0.03 TgCH₄ yr⁻¹) and Equatorial Africa (-0.21 ± 0.04 TgCH₄ yr⁻¹). The pronounced decline over Brazil possibly reflects the exceptional drought conditions in the Amazon basin during 2022–2024, including record-low river levels and anomalously warm, dry conditions (Espinoza et al., 2024). Such hydroclimatic drying reduces floodplain inundation and lowers water tables, which suppresses anaerobic conditions and thereby limits wetland methane production and emissions (Cui et al., 2024).

The emission predictions enable an assessment of long-term trends and interannual variability in wetland emissions over 2000–2025. Annual emission time series show pronounced peaks in 2011 and 2016/2017 in the tropics, NH mid-latitudes, and globally (Fig. S6). Monthly emission anomalies in these regions (Fig. S7) indicate that the annual maxima are driven by sustained positive anomalies during August 2010–May 2011 and August 2016–December 2017, which coincide with La Niña conditions. This alignment is consistent with prior studies reporting La Niña-driven wetting and expanded inundation that enhance tropical wetland CH₄ emissions (Hodson et al., 2011; Hasan et al., 2022; Nisbet et al., 2023; Lin et al., 2024; Murguia-Flores et al., 2023; Zhang et al., 2020, 2018; Zhu et al., 2017), and with the broader evidence that CH₄ growth in 2020–2022 coincided with an unusual persistent La Niña event.

In the 2020s, global and tropical annual emissions reach a minimum in 2023 (Figs. S6, S7), coincident with a strong El Niño event. This is supported by two recent studies reporting a sharp decline in wetland emissions in South America (Ciais et al., 2026) and Amazonia (Quinn et al., 2025) in 2023 linked to El Niño-related drought. Our results further suggest that Brazil and Southwest South America are the key contributors, and both rank among the top six emitting regions globally (Fig. 5). Annual emissions in Brazil decline steadily across the study period, from 24.8±0.9 TgCH₄ yr⁻¹ in 2000 to 23.3 ± 0.9 TgCH₄ yr⁻¹ in 2025, with the lowest emission in 2024 (22.8 ± 0.9 TgCH₄ yr⁻¹) (Fig. 5). Emissions in Southwest South America are relatively stable during 2000–2014, decrease markedly during 2014–2023 (from 17.2 ± 0.6 to 15.6±0.6 TgCH₄ yr⁻¹), and show a modest recovery in 2024/2025 (Fig. 5).

The remaining four top emitting regions show contrasting behavior. Since 2019, Southeast Asia, Canada and Russia show increasing emissions, whereas Equatorial Africa emissions decrease slightly (Fig. 5). Southeast Asian emissions fluctuate around 24.7 ± 0.5 TgCH₄ yr⁻¹ over 2000–2025, with a minimum in 2015 (23.8 ± 0.7 TgCH₄ yr⁻¹) and a maximum in 2017 (25.6 ± 0.7 TgCH₄ yr⁻¹). Emissions in Canada are comparatively stable during 2005–2015, while variability is larger before 2005 and after 2015: the amplitude reaches ∼ 1.5 TgCH₄ yr⁻¹ in these periods, approximately double that during 2005–2015 (∼ 0.8 TgCH₄ yr⁻¹). Equatorial African emissions remain 17.0 ± 0.2 TgCH₄ yr⁻¹ during 2000–2025, despite two notable declines during 2002–2005 (-0.5 TgCH₄ yr⁻¹) and 2019–2025 (-0.6 TgCH₄ yr⁻¹).

For regions showing increasing emissions in our XGBoost predictions (e.g., Southeast Asia, Canada, and Russia), these changes are likely driven by wetter and/or warmer conditions that enhance inundation, substrate supply, and CH₄ production. For instance, observed increases in Boreal-Arctic wetland CH₄ emissions have been linked to warming and enhanced ecosystem productivity (Yuan et al., 2024), while inversion-based studies highlight the role of La Niña-driven inundation in enhancing emissions from Equatorial Asia (Qu et al., 2024; Lin et al., 2024; Feng et al., 2022). However, our emulator predictions diverge from recent atmospheric inversion literature regarding African wetland emissions. Multiple top-down studies indicate that enhanced emissions from tropical Africa contributed substantially to the global CH₄ surge during 2020–2024 (Balasus et al., 2026; Qu et al., 2024). In contrast, our climate-driven emulator predicts that emissions across Northern, Equatorial, and Southern Africa remained relatively stable or slightly decreased during similar time periods (Figs. 4, 5, S8). We attribute this discrepancy to weaker performance and regional precipitation biases of ERA5 data over parts of Africa in capturing complex African hydrological dynamics (Gebrechorkos et al., 2024).

Regional emission time series (Figs. 5, S8) help explain the pronounced disagreement in 2019–2020 for global and tropical emissions, where predictions are substantially lower than the GMB estimates (Fig. S6). For 2019, the prediction mean emissions are 5.5 TgCH₄ yr⁻¹ below the GMB estimates for global emissions (∼ 3.5 % underestimation), while emission changes in Equatorial Africa, Brazil, USA, Southern Africa, and Southwest South America jointly account for 4.3 TgCH₄ yr⁻¹ of this discrepancy. In 2020, the modeled mean global emissions are 9.8 TgCH₄ yr⁻¹ lower than GMB (∼ 6 % underestimation). The discrepancies in the tropics explain 9.2 TgCH₄ yr⁻¹ of the underestimation, including major regions such as Eastern Africa, Southwest South America, Southeast Asia, Brazil, and Northern Africa. Notably, this disagreement in 2019–2020 is not evident in other latitude bands and does not persist outside these years. We cautiously interpret this mismatch as the limitations of the emulator and uncertainty in the underlying GMB ensemble. The climate predictors used in this study can only indirectly capture ENSO (El Niño-Southern Oscillation) effects. Although the training period includes several ENSO events, the unusual persistence of the 2020–2023 triple-dip La Niña may still be underrepresented. In addition, most TD estimates from GMB use a similar prescribed OH (hydroxyl radical) distribution, possibly causing OH-related uncertainty to be underrepresented, particularly during anomalous periods such as COVID.

Finally, we examine trends in predicted monthly wetland CH₄ emissions across five latitude bands (Fig. 6a) and 12 calendar-month categories (Fig. 6b). Figure 6a compares these trends across three time periods (2000–2025, 2021–2025, and 2000–2020). Figure 6b shows the trends by 12 calendar-month categories for the 2000–2025 period. We summed predicted monthly emissions across all grid cells within each latitude band to form regional monthly time series, then computed monthly emission anomalies by subtracting the 2000–2025 climatological mean. We then estimated trends (Tg CH₄ yr⁻¹) using ordinary least squares linear regression of the anomaly time series. We calculated a z-score for each region and period by dividing the estimated trend by its standard error to assess the statistical significance of the trends.

The global wetland CH₄ emission trend is three times lower in 2021–2025 compared with the 2000–2020 period (0.05 ± 0.28 and 0.15 ± 0.05 Tg CH₄ yr⁻¹, respectively) (Fig. 6a). However, the magnitude of changes in emission trends in NH mid-latitudes, the tropics and SH extratropics is greater in 2021–2025 than 2000–2020 level. Emission trends in NH mid-latitudes increased three times from 0.05 ± 0.02 TgCH₄ yr⁻¹ (z=4.94) in 2000–2020 to 0.16±0.12 TgCH₄ yr⁻¹ (z=2.49) during 2021–2025. The tropics exhibit a reversal of their emission trends from positive in 2000–2020 (0.07 ± 0.03 Tg CH₄ yr⁻¹, z=3.95) to negative in 2021–2025 (-0.18 ± 0.24 Tg CH₄ yr⁻¹, z=-1.45). Emission trends in SH extratropics increased from nearly zero in 2000–2020 (-8.74×10-4 ±5.18×10-3 TgCH₄ yr⁻¹, z=-0.33) to higher than the global average in 2021-2025 (0.04 ± 0.02 TgCH₄ yr⁻¹, z=3.30).

Evaluating emission trends over individual months (Fig. 6b), we find that the global trend peaks in late boreal summer, with the largest increases in August and September (0.15 ± 0.18 and 0.21 ± 0.13 TgCH₄ yr⁻¹, respectively). The strongest seasonal intensification occurs in NH mid-latitudes during June–September (0.09–0.12 Tg CH₄ yr⁻¹), consistent with the dominant contribution from boreal growing-season emissions. For both NH mid- and high-latitudes, growth rates are systematically higher during May–October (growing season) than during November–April. In the tropics, monthly growth reaches its highest in October–December and lowest in June–August (negative growth). SH extratropics show near-zero growth throughout the year (slightly negative).

A limitation concerns source attribution in the TD estimates used for training. Although the TD inversions adopted BU wetland priors that exclude inland freshwater systems, the coarse spatial resolution of TD inversions can still limit the separation of natural vegetated wetland emissions from nearby open-water or inland-freshwater emissions in mixed floodplain environments. This uncertainty cannot be quantified explicitly in the present framework because no independent source-resolved posterior diagnostic is available. Future progress is likely to come from higher-resolution atmospheric inverse frameworks. Two recent studies performed 25 km resolution atmospheric inversions using satellite observations in regional and global applications and improved spatial attribution (East et al., 2025; Hancock et al., 2025).

Another limitation is that our framework predicts wetland emissions from climate predictors only. ENSO-related impacts can be captured only indirectly, while dynamic inundation and atmospheric chemistry perturbations are outside the predictor space of the model.