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<front>
<journal-meta>
<journal-id journal-id-type="publisher">ESSDD</journal-id>
<journal-title-group>
<journal-title>Earth System Science Data Discussions</journal-title>
<abbrev-journal-title abbrev-type="publisher">ESSDD</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">Earth Syst. Sci. Data Discuss.</abbrev-journal-title>
</journal-title-group>
<publisher><publisher-name></publisher-name>
<publisher-loc>Göttingen, Germany</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.5194/essd-2026-848</article-id>
<title-group>
<article-title>A near-real-time monthly dataset of CO&lt;sub&gt;2&lt;/sub&gt;, CH&lt;sub&gt;4&lt;/sub&gt;, and N&lt;sub&gt;2&lt;/sub&gt;O emissions from fossil fuel combustion</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Niu</surname>
<given-names>Hanlin</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff5">
<sup>5</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Fu</surname>
<given-names>Jiaying</given-names>
<ext-link>https://orcid.org/0009-0002-5567-4435</ext-link>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff5">
<sup>5</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Huang</surname>
<given-names>Miao</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Li</surname>
<given-names>Xi</given-names>
<ext-link>https://orcid.org/0000-0002-6725-9119</ext-link>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Meng</surname>
<given-names>Xianhong</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Yuan</surname>
<given-names>Wenping</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Piao</surname>
<given-names>Shilong</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>Institute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China</addr-line>
</aff>
<aff id="aff2">
<label>2</label>
<addr-line>School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China</addr-line>
</aff>
<aff id="aff3">
<label>3</label>
<addr-line>Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China</addr-line>
</aff>
<aff id="aff4">
<label>4</label>
<addr-line>Zoige Plateau Wetlands Ecosystem Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China</addr-line>
</aff>
<aff id="aff5">
<label>5</label>
<addr-line>These authors contributed equally to this work.</addr-line>
</aff>
<pub-date pub-type="epub">
<day>05</day>
<month>11</month>
<year>2026</year>
</pub-date>
<volume>2026</volume>
<fpage>1</fpage>
<lpage>22</lpage>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2026 Hanlin Niu et al.</copyright-statement>
<copyright-year>2026</copyright-year>
<license license-type="open-access">
<license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri"  xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p>
</license>
</permissions>
<self-uri xlink:href="https://essd.copernicus.org/preprints/essd-2026-848/">This article is available from https://essd.copernicus.org/preprints/essd-2026-848/</self-uri>
<self-uri xlink:href="https://essd.copernicus.org/preprints/essd-2026-848/essd-2026-848.pdf">The full text article is available as a PDF file from https://essd.copernicus.org/preprints/essd-2026-848/essd-2026-848.pdf</self-uri>
<abstract>
<p>Fuel-combustion greenhouse gas emissions account for about 67 % of the total anthropogenic emissions, and thus reliable estimates lay the foundation for the Global Stocktake, the evaluation of nationally determined contributions (NDCs), and the quantification of remaining carbon budgets. However, to fully understand the current status of greenhouse gas emissions and to formulate policies for effective control of greenhouse gas emissions, a dataset that provides a comprehensive, up-to-date, and sector-specific record of greenhouse gases is needed. This paper presents a global dataset covering CO&lt;sub&gt;2&lt;/sub&gt;, CH&lt;sub&gt;4&lt;/sub&gt;, and N&lt;sub&gt;2&lt;/sub&gt;O from 1960 to 2025. The dataset reports emissions classified by country, sector, fuel, and gas at monthly resolution, covering emissions from fuel combustion. It covers 153 reporting entities, which consist of 150 individually reported countries, one residual entity covering 48 remaining countries and territories, international aviation, and international shipping. Historical emissions from 1960&amp;ndash;2023 are reconstructed using emission factors and statistical data of energy consumption derived from International Energy Agency (IEA) energy activity dataset. For the latest two years without &lt;span&gt;available IEA activity data&lt;/span&gt;, we constructed a framework for predicting emissions based on historical emissions data and &lt;span&gt;monthly activity indicators from CEIC&lt;/span&gt;. We evaluate six individual prediction methods and their ensembles using historical emissions from 2019 to 2023 and select the best prediction method for each specific country, sector, and gas combination. The best method is applied to estimate annual emissions for 2024&amp;ndash;2025. In addition, we distribute the annual emission values to monthly values by using monthly activity patterns. The dataset demonstrates that CO&lt;sub&gt;2&lt;/sub&gt; and N&lt;sub&gt;2&lt;/sub&gt;O show overall increasing trends with CO&lt;sub&gt;2&lt;/sub&gt; reaching 37.7 Gt CO&lt;sub&gt;2&lt;/sub&gt; yr&lt;sup&gt;-1&lt;/sup&gt; in 2025 and N&lt;sub&gt;2&lt;/sub&gt;O reaching 1,057 kt N&lt;sub&gt;2&lt;/sub&gt;O yr&lt;sup&gt;-1&lt;/sup&gt; in 2025. In contrast, CH&lt;sub&gt;4 &lt;/sub&gt;reached its peak around 1990 and then declined and &lt;span&gt;remained&lt;/span&gt; close to 11 Mt CH&lt;sub&gt;4 &lt;/sub&gt;yr&lt;sup&gt;-1&lt;/sup&gt;. Emissions of all three gases also follow a seasonal pattern with emissions being highest in winter and lowest in summer. For validation purposes, we compare our dataset with existing datasets and find good overall agreement. This dataset supports carbon budget tracking, NDC and Global Stocktake assessment, and near-real-time monitoring of sectoral greenhouse gas emissions.&amp;nbsp;</p>
</abstract>
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<funding-group>
<award-group id="gs1">
<funding-source>National Key Research and Development Program of China</funding-source>
<award-id>2025YFF0812104</award-id>
</award-group>
</funding-group>
</article-meta>
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