This study presents a new framework (MBEI) that integrates top-down and bottom-up algorithms by combining two fire-detection products with four sets of key input variables, yielding eight distinct sub-inventories of biomass-burning emissions. Compared to existing inventories, these new datasets uniquely provide the maximum–minimum range of all eight sub-inventories, thereby quantifying estimation uncertainty. This rich information allows data users to directly incorporate sensitivity analyses into their own studies and thus provides critical support for exploring complex global biomass-burning dynamics. The paper is well written and offers new insight into biomass-burning research. However, a few points need to be addressed. Therefore, I recommend that the manuscript be accepted for publication after a revision.

Major concerns:

1. Although the new dataset includes eight groups of sub-inventories, the mean values and uncertainty information (e.g., standard deviation, minimum, and maximum) for these ensemble inventories are not provided at the pixel scale. I suggest that the authors calculate these statistics on a per-pixel basis and include them in the dataset. This information is highly valuable to data users and should be made publicly available.
2. In this study, only MODIS fire products are used, while the higher-resolution VIIRS is not considered. As the successor to MODIS, VIIRS offers enhanced spatial resolution (375–750 m) and advanced day-night imaging capabilities for superior global monitoring of land, ocean, and atmospheric phenomena. The authors should explicitly state the reason for not including VIIRS observations in the manuscript.
3. To address the limitation of a static AGB benchmark map, net primary productivity (NPP) is used as a proxy to generate annual global AGB maps. Does this approach introduce inherent uncertainties? The authors should discuss these.
4. The abstract is overly long and information-dense. I suggest to keep the most important findings and shorten the abstract to make the expression more concise.
5. The Discussion section should be strengthened. Other factors are likely to bring the uncertainty in the estimation of biomass burning emission, for example the omission and commission errors in the satellite fire observations. Meanwhile, the existing bottom-up and up-down method is like to underestimate the emission for small-scale fire.

Minor comments:

1. Line 67-68: add references to support this statement.
2. Lines 90–91: An extraneous period appears after “Karanasiou et al (2021)”. Please remove it to ensure proper punctuation.
3. There are some grammar issues and please proofread the entire manuscript carefully before resubmission. For example, Line 102-103, “Atmospheric Chemistry Transport Models used for air quality forecasting and source apportionment, rely on emission inventories with high spatiotemporal resolution and reliability”.
4. Line 182: The current format is cluttered and hard to read. Please reformat it.
5. Figures 2a and 3a,b: Fonts are too small, reducing readability. Please enlarge them and ensure consistent styling across panels.
6. Two extraneous spaces appear between “high” and “biomass”. Please delete them for clean typesetting.
7. Table 3: please do not use the short name for maximum, minimum, and average.
8. Line 469: use a consistent format for the term “max-min band”.
9. Line 477-479: Is there any tables or figures in the supplementary material to support these results?
10. Supplementary material: Please ensure all figures and tables are explicitly referenced in the main text.

The research entitled " The newly developed Multi-ensemble Biomass-burning Emissions Inventory (MBEI): Characterizing and unraveling spatiotemporal uncertainty in global biomass burning emissions" develops the Multi-ensemble Biomass-burning Emissions Inventory (MBEI), a global emission dataset for 2003–2023, by integrating multi-source data and bottom-up/top-down approaches. It quantifies spatiotemporal uncertainty and reveals heterogeneous emission trends, providing a robust framework for atmospheric and climate analyses. The article is well-written and demonstrates strong logical coherence. However, a few points need to be addressed.

1. How were the considerations addressed for the selection of multi-source data in this study? Specifically, as shown in Table 1, the temporal resolutions of the listed data sources vary significantly. How were annual-average data interpolated into monthly-average values?
2. Section 2.2 provides detailed descriptions of the bottom-up and top-down approaches. However, the explanation of the integrated approach employed in this study remains insufficiently clear. Please provide further clarification.
3. As noted by the authors, significant discrepancies exist among different emission inventories. In integrating the bottom-up and top-down approaches in this study, how were the inherent differences between these two methodologies accounted for? Could these uncertainties propagate into the integrated results and consequently affect the accuracy of MBEI?
4. A detailed explanation should be provided on how the uncertainty in global biomass burning emissions is quantified.