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  <front>
    <journal-meta><journal-id journal-id-type="publisher">ESSD</journal-id><journal-title-group>
    <journal-title>Earth System Science Data</journal-title>
    <abbrev-journal-title abbrev-type="publisher">ESSD</abbrev-journal-title><abbrev-journal-title abbrev-type="nlm-ta">Earth Syst. Sci. Data</abbrev-journal-title>
  </journal-title-group><issn pub-type="epub">1866-3516</issn><publisher>
    <publisher-name>Copernicus Publications</publisher-name>
    <publisher-loc>Göttingen, Germany</publisher-loc>
  </publisher></journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.5194/essd-18-2689-2026</article-id><title-group><article-title>A high-resolution tropopause folding dataset over China from 2014–2023</article-title><alt-title>A high-resolution tropopause folding dataset over China from 2014–2023</alt-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Yang</surname><given-names>Yujia</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="yes" rid="aff1">
          <name><surname>Cao</surname><given-names>Le</given-names></name>
          <email>le.cao@nuist.edu.cn</email>
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Xu</surname><given-names>Liqiang</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Wang</surname><given-names>Mengke</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1 aff3">
          <name><surname>Yang</surname><given-names>Qingjian</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Zhang</surname><given-names>Yuqing</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2">
          <name><surname>Zhang</surname><given-names>Tianqi</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2">
          <name><surname>Lei</surname><given-names>Xiuli</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Miao</surname><given-names>Jiangpeng</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Zhao</surname><given-names>Tianliang</given-names></name>
          
        </contrib>
        <aff id="aff1"><label>1</label><institution>State Key  Laboratory of Climate System Prediction and Risk Management,  Nanjing University of Information Science and Technology, Nanjing, 210044, China</institution>
        </aff>
        <aff id="aff2"><label>2</label><institution>National Supercomputer Center in Tianjin, Tianjin, 300457,  China</institution>
        </aff>
        <aff id="aff3"><label>3</label><institution>National Meteorological Information Center, China Meteorological Administration, Beijing, 100081, China</institution>
        </aff>
      </contrib-group>
      <author-notes><corresp id="corr1">Le Cao (le.cao@nuist.edu.cn)</corresp></author-notes><pub-date><day>17</day><month>April</month><year>2026</year></pub-date>
      
      <volume>18</volume>
      <issue>4</issue>
      <fpage>2689</fpage><lpage>2702</lpage>
      <history>
        <date date-type="received"><day>3</day><month>November</month><year>2025</year></date>
           <date date-type="rev-request"><day>19</day><month>February</month><year>2026</year></date>
           <date date-type="rev-recd"><day>30</day><month>March</month><year>2026</year></date>
           <date date-type="accepted"><day>31</day><month>March</month><year>2026</year></date>
      </history>
      <permissions>
        <copyright-statement>Copyright: © 2026 Yujia Yang 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/articles/18/2689/2026/essd-18-2689-2026.html">This article is available from https://essd.copernicus.org/articles/18/2689/2026/essd-18-2689-2026.html</self-uri><self-uri xlink:href="https://essd.copernicus.org/articles/18/2689/2026/essd-18-2689-2026.pdf">The full text article is available as a PDF file from https://essd.copernicus.org/articles/18/2689/2026/essd-18-2689-2026.pdf</self-uri>
      <abstract><title>Abstract</title>

      <p id="d2e180">Tropopause folds are a primary mechanism for stratosphere-to-troposphere transport (STT), significantly influencing tropospheric ozone, air quality, and regional weather. However, a multi-year, high-resolution folding dataset for China has been lacking. To address this gap, we developed a comprehensive tropopause folding dataset for China and its surrounding regions using the ERA5 reanalysis data and a three-dimensional labeling method for the period 2014–2023. This dataset is distinguished by its 10-year long coverage (2014–2023) and an unprecedented combination of high spatial (0.25° <inline-formula><mml:math id="M1" display="inline"><mml:mo>×</mml:mo></mml:math></inline-formula> 0.25°) and temporal (hourly) resolutions, which collectively facilitate a detailed analysis of the spatiotemporal characteristics of folds in these regions. We validated the dataset's accuracy and its capability to capture the full evolution of folding events by comparing it with global datasets and multiple case studies of stratospheric intrusions. The high-resolution dataset not only aligns with large-scale climatology from previous studies but also resolves finer details that were previously smoothed out. Furthermore, this dataset reveals the distinct spatiotemporal distribution of tropopause folds over China. Folding events over China were found to occur predominantly in winter and spring, with significantly lower frequencies in summer and autumn. Spatially, shallow folds are concentrated along the subtropical jet stream, while medium folds peak over the Tibetan Plateau (TP) and Sichuan Basin (SCB) in winter. Deep folds, though less frequent, exert a notable impact on southwestern China, including the TP, where they can substantially affect surface ozone levels. This dataset is available at <ext-link xlink:href="https://doi.org/10.5281/zenodo.17446871" ext-link-type="DOI">10.5281/zenodo.17446871</ext-link>
<xref ref-type="bibr" rid="bib1.bibx9" id="paren.1"/>, and provides a solid foundation for in-depth investigations into the mechanisms and climatological characteristics of STT, as well as the subsequent impacts of tropopause folds on near-surface atmospheric composition and extreme weather events, thereby serving as a valuable resource for the atmospheric science community.</p>
  </abstract>
    
<funding-group>
<award-group id="gs1">
<funding-source>National Key Research and Development Program of China</funding-source>
<award-id>2022YFC3701204</award-id>
</award-group>
<award-group id="gs2">
<funding-source>National Natural Science Foundation of China</funding-source>
<award-id>41705103</award-id>
</award-group>
</funding-group>
</article-meta>
  </front>
<body>
      

<sec id="Ch1.S1" sec-type="intro">
  <label>1</label><title>Introduction</title>
      <p id="d2e205">“Tropopause fold” is  a high-altitude atmospheric phenomenon in which the tropopause (the boundary between troposphere and overlying stratosphere) is perturbed to fold downward, enabling stratospheric air to intrude into the troposphere <xref ref-type="bibr" rid="bib1.bibx32 bib1.bibx6" id="paren.2"/>. This phenomenon emerges when a portion of the lowermost stratosphere is transported equatorward, forcing high-potential-temperature air masses to move along sloping isentropes <xref ref-type="bibr" rid="bib1.bibx11 bib1.bibx37 bib1.bibx15" id="paren.3"/>, resulting in a penetration of dry, ozone-rich air across the tropopause <xref ref-type="bibr" rid="bib1.bibx45 bib1.bibx2 bib1.bibx18 bib1.bibx13" id="paren.4"/>. Tropopause folds constitute the primary mechanism driving the stratosphere-to-troposphere transport (STT) <xref ref-type="bibr" rid="bib1.bibx37" id="paren.5"/>.  STT is the general process of air mass transport from the stratosphere to the troposphere, playing a critical role in introducing ozone-rich stratospheric air into the troposphere <xref ref-type="bibr" rid="bib1.bibx16 bib1.bibx37 bib1.bibx18" id="paren.6"/>. When the folds occur, STT manifests as stratospheric intrusions (SIs), characterized by their episodic nature and rapid descent, significantly altering the total tropospheric ozone burden <xref ref-type="bibr" rid="bib1.bibx40 bib1.bibx28" id="paren.7"/>. Beyond ozone transport, recent studies also indicate that STT in the Arctic injects ozone-depleting precursors (such as bromine and iodine) and stratospheric aerosols into the free troposphere, influencing local tropospheric chemistry <xref ref-type="bibr" rid="bib1.bibx31" id="paren.8"/>. Furthermore, when sufficiently deep, tropopause folds can even extend into the atmospheric boundary layer, directly altering near-surface concentrations of air constituents such as ozone and carbon monoxide (CO) <xref ref-type="bibr" rid="bib1.bibx45 bib1.bibx23 bib1.bibx2 bib1.bibx1" id="paren.9"/>. Apart from chemical impacts, the tropopause folds, which are often linked to rapid cyclogenesis and convective storms <xref ref-type="bibr" rid="bib1.bibx39 bib1.bibx38 bib1.bibx4 bib1.bibx12" id="paren.10"/>, can strongly affect local weather conditions through a downward transport of air with high potential vorticity <xref ref-type="bibr" rid="bib1.bibx3" id="paren.11"/>. For instance, extreme surface winds can be driven by the tropopause folds due to the downward transport of momentum <xref ref-type="bibr" rid="bib1.bibx29 bib1.bibx8" id="paren.12"/>. Strong upward motions, generated in the downstream region of tropopause folds, can also promote precipitation <xref ref-type="bibr" rid="bib1.bibx20" id="paren.13"/>. Therefore, the tropopause fold usually acts as a bridge linking stratospheric anomalies to surface extreme events <xref ref-type="bibr" rid="bib1.bibx47" id="paren.14"/>.</p>
      <p id="d2e249">Tropopause folds demonstrate clear temporal and spatial variations in their distribution. Temporally, tropopause folding events exhibit distinct seasonality, occurring more frequently in winter but less frequently in summer  <xref ref-type="bibr" rid="bib1.bibx3 bib1.bibx34 bib1.bibx36" id="paren.15"/>. Spatially, shallow folding events predominantly occur along the subtropical jet stream (STJ) in both hemispheres, particularly in regions characterized by strong wind speeds or intense Rossby wave breaking. Medium folding events, by contrast, reach peak frequency in polar regions and the subtropics, along mid-latitude storm tracks. With respect to deep folding events, they exhibit distinct hotspots over western Australia, coastal Antarctica, and the eastern coast of North America <xref ref-type="bibr" rid="bib1.bibx36 bib1.bibx34 bib1.bibx3 bib1.bibx4 bib1.bibx24" id="paren.16"/>.</p>
      <p id="d2e258">China and its surrounding regions, substantially influenced by the STJ <xref ref-type="bibr" rid="bib1.bibx5" id="paren.17"/>, are high-frequency regions for shallow and medium tropopause folds. Meanwhile, regions such as the Tibetan Plateau (TP) in the southwestern China are prone to be affected by deep folding events, which can remarkably impact surface ozone levels in these regions <xref ref-type="bibr" rid="bib1.bibx43 bib1.bibx25 bib1.bibx22" id="paren.18"/>. Therefore, investigating tropopause folds across China and its surroundings is essential for understanding their distribution characteristics and impacts on atmospheric composition and weather systems in these regions.</p>
      <p id="d2e267">However, a multi-year folding dataset with high temporal and spatial resolutions for China is still lacking. Global folding datasets spanning long time periods, as provided in large-scale studies <xref ref-type="bibr" rid="bib1.bibx24 bib1.bibx34 bib1.bibx7" id="paren.19"/>, typically  have a coarse horizontal resolution (<inline-formula><mml:math id="M2" display="inline"><mml:mo lspace="0mm">&gt;</mml:mo></mml:math></inline-formula> 100 km). For instance, the Atmospheric Dynamics group at ETH Zürich provides a global folding dataset (<uri>http://eraiclim.ethz.ch/prot/folds.html</uri>, last access: 21 September 2025)  for years 1980–2014, calculated from the ERA-Interim reanalysis data. However, this dataset gives the folding data in a time scale of month, and the folding data possess a horizontal resolution of 1°  (<inline-formula><mml:math id="M3" display="inline"><mml:mo lspace="0mm">∼</mml:mo></mml:math></inline-formula>110 km). In contrast, a horizontal resolution of at least 50 km is necessary to resolve folding morphology and associated turbulent STT processes <xref ref-type="bibr" rid="bib1.bibx35 bib1.bibx17" id="paren.20"/>. On the other side,  while folding datasets with high spatial and temporal resolutions have been utilized in case analyses <xref ref-type="bibr" rid="bib1.bibx44 bib1.bibx43 bib1.bibx47 bib1.bibx22" id="paren.21"/>, these studies have been limited to restricted spatial domains and short time periods. For instance, <xref ref-type="bibr" rid="bib1.bibx47" id="text.22"/> investigated the influence of tropopause folds on an extreme low-temperature event in Eastern China in January 2021, by applying a 3-D labeling methodology on  0.25° <inline-formula><mml:math id="M4" display="inline"><mml:mo>×</mml:mo></mml:math></inline-formula> 0.25° ERA5 (ECMWF Reanalysis version 5)  reanalysis data. They found that tropopause folds facilitated the intrusion of cold air from the lower Arctic stratosphere into the troposphere, contributing to the surface cooling across Eastern China. However, their findings are mainly specific to this particular event and  time period. <xref ref-type="bibr" rid="bib1.bibx22" id="text.23"/> provided a detailed analysis of a tropopause fold event on 11 July 2020, confirming its role in transporting stratospheric ozone-rich air into the planetary boundary layer over the TP. Through a long-term assessment, they further established that tropopause folding events lead to a significant increase in surface ozone concentrations across the TP, averaging a 15.94 ppbv rise.  However, their study was geographically confined to the TP region. In addition, the underlying folding datasets used in these case studies are usually not provided to the public.</p>
      <p id="d2e311">To address this issue, we developed a multi-year high-resolution tropopause folding dataset for China and its surrounding regions. This dataset covers a time period of 10 years (2014–2023), and has a spatial coverage from 50 to 140° E and 0 to 70° N. Regarding the resolutions, the folding data provided by this dataset possess an hourly temporal resolution and a horizontal resolution of 0.25° <inline-formula><mml:math id="M5" display="inline"><mml:mo>×</mml:mo></mml:math></inline-formula> 0.25° (<inline-formula><mml:math id="M6" display="inline"><mml:mo lspace="0mm">∼</mml:mo></mml:math></inline-formula> 28 km). Compared to existing folding datasets, our folding dataset is distinguished by its combination of extensive temporal coverage and high spatiotemporal resolution over China and its surrounding regions. This dataset facilitates the detailed analysis of spatiotemporal variations in tropopause folds over China.  Moreover, it establishes a foundation for investigating STT processes and the subsequent impacts on near-surface ozone concentrations and regional extreme weather events. Additionally, this dataset serves as a critical resource for climate research on STT.</p>
</sec>
<sec id="Ch1.S2">
  <label>2</label><title>Meteorological Data and Methods</title>
      <p id="d2e336">This section provides a comprehensive description of the employed meteorological data and the methodology for identifying tropopause folds based on these meteorological data.</p>
<sec id="Ch1.S2.SS1">
  <label>2.1</label><title>Meteorological Data</title>
      <p id="d2e346">We utilized the ERA5 meteorological reanalysis dataset  <xref ref-type="bibr" rid="bib1.bibx14" id="paren.24"/> to identify tropopause folds and quantify their occurrence frequency over China. ERA5 dataset, provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), is the fifth-generation global atmospheric reanalysis product for climate. It offers high-resolution meteorological data spanning from 1940 to present. In this study, we adopted the ERA5 hourly   dataset, from 1 January 2014 to 31 December  2023, with a 0.25° <inline-formula><mml:math id="M7" display="inline"><mml:mo>×</mml:mo></mml:math></inline-formula> 0.25° horizontal resolution, and 37 vertical pressure levels. Among various meteorological variables provided by the ERA5 dataset, we used potential vorticity (PV), potential temperature, specific humidity, and surface pressure to identify the folding events.</p>
</sec>
<sec id="Ch1.S2.SS2">
  <label>2.2</label><title>Identification of Tropopause Folds</title>
      <p id="d2e367">Tropopause folds were identified using a three-dimensional labeling method proposed by <xref ref-type="bibr" rid="bib1.bibx34" id="text.25"/>, developed based on the study of <xref ref-type="bibr" rid="bib1.bibx36" id="text.26"/>. This method identifies the tropopause folds geometrically as multiple crossings of the dynamical tropopause within vertical atmospheric profiles. It first determines the altitude of the dynamical tropopause by selecting the lower boundary between the 2 PVU potential vorticity isosurface and the 380 K potential temperature isosurface <xref ref-type="bibr" rid="bib1.bibx15 bib1.bibx36 bib1.bibx37" id="paren.27"/>. Next, the method assigns various types of the air with five labels (see Fig. <xref ref-type="fig" rid="F1"/>):  1 <inline-formula><mml:math id="M8" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> troposphere, 2 <inline-formula><mml:math id="M9" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> stratosphere, 3 <inline-formula><mml:math id="M10" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> stratospheric cutoff or diabatically produced cyclonic PV anomaly, 4 <inline-formula><mml:math id="M11" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> tropospheric cutoff, and 5 <inline-formula><mml:math id="M12" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> surface-bound cyclonic PV anomaly. Label 1 denotes typical tropospheric air, characterized by low PV values (<inline-formula><mml:math id="M13" display="inline"><mml:mo lspace="0mm">&lt;</mml:mo></mml:math></inline-formula> 2 PVU). Label 2 represents stratospheric air, defined by high PV (<inline-formula><mml:math id="M14" display="inline"><mml:mo lspace="0mm">&gt;</mml:mo></mml:math></inline-formula>2 PVU), low specific humidity (<inline-formula><mml:math id="M15" display="inline"><mml:mo lspace="0mm">≤</mml:mo></mml:math></inline-formula> 0.1 g kg<sup>−1</sup>), and three-dimensional connectivity with the stratospheric reservoir. Label 3 corresponds to stratospheric cutoffs, which are isolated stratospheric air masses suspended in the troposphere, or high-PV tropospheric anomalies resulting from dissipative processes, characterized by high specific humidity (<inline-formula><mml:math id="M17" display="inline"><mml:mo lspace="0mm">&gt;</mml:mo></mml:math></inline-formula> 0.1 g kg<sup>−1</sup>). Label 4 indicates tropospheric cutoffs, defined as isolated tropospheric air masses with low PV surrounded by stratospheric air. Label 5 identifies surface-bound PV anomalies, distinguished by high PV values extending down to the surface. After constructing a three-dimensional field of air labels, a folding event is  identified when a grid node exhibits either a 2 <inline-formula><mml:math id="M19" display="inline"><mml:mo>→</mml:mo></mml:math></inline-formula> 1 <inline-formula><mml:math id="M20" display="inline"><mml:mo>→</mml:mo></mml:math></inline-formula> 2 <inline-formula><mml:math id="M21" display="inline"><mml:mo>→</mml:mo></mml:math></inline-formula> 1 or 2 <inline-formula><mml:math id="M22" display="inline"><mml:mo>→</mml:mo></mml:math></inline-formula> 1 <inline-formula><mml:math id="M23" display="inline"><mml:mo>→</mml:mo></mml:math></inline-formula> 2 <inline-formula><mml:math id="M24" display="inline"><mml:mo>→</mml:mo></mml:math></inline-formula> 3 vertical transition from the top to the bottom of the model. Thus, in this folding dataset, at each grid node, the occurrence of folds is represented by a binary variable <italic>fold</italic>, with a value of 1 indicating the occurrence of a folding event and 0 indicating its absence. Furthermore, the upper, middle, and lower pressure levels at the tropopause crossings are denoted as <inline-formula><mml:math id="M25" display="inline"><mml:mrow><mml:msub><mml:mi>p</mml:mi><mml:mi mathvariant="normal">u</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>, <inline-formula><mml:math id="M26" display="inline"><mml:mrow><mml:msub><mml:mi>p</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>, and <inline-formula><mml:math id="M27" display="inline"><mml:mrow><mml:msub><mml:mi>p</mml:mi><mml:mi mathvariant="normal">l</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>, respectively (see Fig. <xref ref-type="fig" rid="F1"/>). Based on <inline-formula><mml:math id="M28" display="inline"><mml:mrow><mml:mi mathvariant="normal">Δ</mml:mi><mml:mi>p</mml:mi><mml:mo>=</mml:mo><mml:msub><mml:mi>p</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:msub><mml:mo>-</mml:mo><mml:msub><mml:mi>p</mml:mi><mml:mi mathvariant="normal">u</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>, the identified folding events were classified into three categories: shallow folds (<italic>sfold</italic>): <inline-formula><mml:math id="M29" display="inline"><mml:mrow><mml:mn mathvariant="normal">50</mml:mn><mml:mo>≤</mml:mo><mml:mi mathvariant="normal">Δ</mml:mi><mml:mi>p</mml:mi><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">200</mml:mn></mml:mrow></mml:math></inline-formula> hPa, medium folds (<italic>mfold</italic>): <inline-formula><mml:math id="M30" display="inline"><mml:mrow><mml:mn mathvariant="normal">200</mml:mn><mml:mo>≤</mml:mo><mml:mi mathvariant="normal">Δ</mml:mi><mml:mi>p</mml:mi><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">350</mml:mn></mml:mrow></mml:math></inline-formula> hPa, and deep folds (<italic>dfold</italic>): <inline-formula><mml:math id="M31" display="inline"><mml:mrow><mml:mi mathvariant="normal">Δ</mml:mi><mml:mi>p</mml:mi><mml:mo>≥</mml:mo><mml:mn mathvariant="normal">350</mml:mn></mml:mrow></mml:math></inline-formula> hPa. In previous studies <xref ref-type="bibr" rid="bib1.bibx24 bib1.bibx34" id="paren.28"><named-content content-type="pre">e.g.,</named-content></xref>, folding events with <inline-formula><mml:math id="M32" display="inline"><mml:mrow><mml:mi mathvariant="normal">Δ</mml:mi><mml:mi>p</mml:mi><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">50</mml:mn></mml:mrow></mml:math></inline-formula> hPa are usually ignored. However, to provide more information on very shallow folding events, we include these events in our dataset. The <italic>fold</italic> label encompasses all folding events, including those with <inline-formula><mml:math id="M33" display="inline"><mml:mrow><mml:mi mathvariant="normal">Δ</mml:mi><mml:mi>p</mml:mi><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">50</mml:mn></mml:mrow></mml:math></inline-formula> hPa. Based on these labels, the folding frequencies <inline-formula><mml:math id="M34" display="inline"><mml:mrow><mml:mi>f</mml:mi><mml:mo>(</mml:mo><mml:mi>s</mml:mi><mml:mo>,</mml:mo><mml:mi>m</mml:mi><mml:mo>,</mml:mo><mml:mi>d</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> for different types of folding events at each grid node are calculated as follows:

            <disp-formula id="Ch1.E1" content-type="numbered"><label>1</label><mml:math id="M35" display="block"><mml:mrow><mml:mi>f</mml:mi><mml:mo>(</mml:mo><mml:mi>s</mml:mi><mml:mo>,</mml:mo><mml:mi>m</mml:mi><mml:mo>,</mml:mo><mml:mi>d</mml:mi><mml:mo>)</mml:mo><mml:mo>=</mml:mo><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mrow><mml:msub><mml:mi>H</mml:mi><mml:mi mathvariant="normal">fold</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>s</mml:mi><mml:mo>,</mml:mo><mml:mi>m</mml:mi><mml:mo>,</mml:mo><mml:mi>d</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mrow><mml:msub><mml:mi>H</mml:mi><mml:mi mathvariant="normal">total</mml:mi></mml:msub></mml:mrow></mml:mfrac></mml:mstyle></mml:mrow></mml:math></disp-formula>

          where <inline-formula><mml:math id="M36" display="inline"><mml:mrow><mml:msub><mml:mi>H</mml:mi><mml:mi mathvariant="normal">fold</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>s</mml:mi><mml:mo>,</mml:mo><mml:mi>m</mml:mi><mml:mo>,</mml:mo><mml:mi>d</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> represents the total  hours with  occurrence of different folding events, and <inline-formula><mml:math id="M37" display="inline"><mml:mrow><mml:msub><mml:mi>H</mml:mi><mml:mi mathvariant="normal">total</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> denotes the total hours in the studied period.</p>

      <fig id="F1"><label>Figure 1</label><caption><p id="d2e785">Schematic diagram of tropopause fold identification. Labels: 1 <inline-formula><mml:math id="M38" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> troposphere, 2 <inline-formula><mml:math id="M39" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> stratosphere, 3 <inline-formula><mml:math id="M40" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> stratospheric cutoff or diabatically produced cyclonic PV anomaly, 4 <inline-formula><mml:math id="M41" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> tropospheric cutoff, 5 <inline-formula><mml:math id="M42" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> surface-bound cyclonic PV anomaly. Gray shading indicates orography. The pressure levels at the tropopause crossings are denoted as <inline-formula><mml:math id="M43" display="inline"><mml:mrow><mml:msub><mml:mi>p</mml:mi><mml:mi mathvariant="normal">u</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>, <inline-formula><mml:math id="M44" display="inline"><mml:mrow><mml:msub><mml:mi>p</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>, and <inline-formula><mml:math id="M45" display="inline"><mml:mrow><mml:msub><mml:mi>p</mml:mi><mml:mi mathvariant="normal">l</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>  for the upper, middle, and lower levels, respectively.</p></caption>
          <graphic xlink:href="https://essd.copernicus.org/articles/18/2689/2026/essd-18-2689-2026-f01.png"/>

        </fig>

</sec>
</sec>
<sec id="Ch1.S3">
  <label>3</label><title>Results and Discussions</title>
      <p id="d2e872">In this section, we first validated our dataset by comparing the spatiotemporal distribution of tropopause folds with that reported in previous global studies. Following validation, we investigated the distributions of different fold types across China and its surrounding regions, analyzing their similarities and differences based on our dataset. Additionally, we adopted three prior case studies on tropopause folds to further verify the accuracy of our dataset in identifying short-term folding events. As demonstrated in following Sect. 3.2.1–3.2.3, our dataset can successfully capture the complete evolution of tropopause folds during stratospheric intrusion events. This capability allows for in-depth investigations of the links between these folds and their associated meteorological drivers, such as upper-level troughs and jet streams, as well as their subsequent impacts on atmospheric composition and near-surface weather conditions.</p>
<sec id="Ch1.S3.SS1">
  <label>3.1</label><title>Spatiotemporal Distribution of Tropopause Folds over China</title>
      <p id="d2e882">Figure <xref ref-type="fig" rid="F2"/> shows the seasonal distributions of folding frequencies over the 2014–2023 period, derived from our dataset. Here, the displayed folding frequency refers to the sum of shallow, medium, and deep folding events,  to facilitate comparison with existing studies. It is seen that over China, folds  occur predominantly during winter (DJF) and spring (MAM), but possess remarkably lower frequencies in summer (JJA) and the lowest frequency in autumn (SON). More specifically, during winter (see Fig. <xref ref-type="fig" rid="F2"/>a), a folding belt spanning the entire China (20–35° N, 70–135° E) is observed, with the maximum frequency exceeding 18 %. In spring (Fig. <xref ref-type="fig" rid="F2"/>b), the southern TP and northern India are identified as key folding regions, with frequencies peaking at approximately 18.7 %, the highest among all seasons. In summer (Fig. <xref ref-type="fig" rid="F2"/>c), the folding frequency in China decreases, and the maximum frequency is found in Central Asia. The folding frequency reaches its seasonal minimum in autumn (Fig. <xref ref-type="fig" rid="F2"/>d), reaching a maximum of <inline-formula><mml:math id="M46" display="inline"><mml:mo>∼</mml:mo></mml:math></inline-formula> 7.3 %. Generally, these seasonal spatiotemporal distributions of folds, provided by our dataset, are consistent with those from previous global studies <xref ref-type="bibr" rid="bib1.bibx24 bib1.bibx7" id="paren.29"/>. However, we found the maximum values of folding frequency in our dataset higher than those obtained by <xref ref-type="bibr" rid="bib1.bibx24" id="text.30"/> using the 1°<inline-formula><mml:math id="M47" display="inline"><mml:mo>×</mml:mo></mml:math></inline-formula>1° ERA5 dataset for 1979–2020. The relative difference in folding frequency <inline-formula><mml:math id="M48" display="inline"><mml:mi>f</mml:mi></mml:math></inline-formula> between our dataset and that of <xref ref-type="bibr" rid="bib1.bibx24" id="text.31"/> is calculated as <inline-formula><mml:math id="M49" display="inline"><mml:mrow><mml:mo>(</mml:mo><mml:msub><mml:mi>f</mml:mi><mml:mtext>our dataset</mml:mtext></mml:msub><mml:mo>-</mml:mo><mml:msub><mml:mi>f</mml:mi><mml:mtext>Lin</mml:mtext></mml:msub><mml:mo>)</mml:mo><mml:mo>/</mml:mo><mml:msub><mml:mi>f</mml:mi><mml:mtext>Lin</mml:mtext></mml:msub></mml:mrow></mml:math></inline-formula>, and it is largest (<inline-formula><mml:math id="M50" display="inline"><mml:mo lspace="0mm">∼</mml:mo></mml:math></inline-formula> 87 %) in spring and smallest (<inline-formula><mml:math id="M51" display="inline"><mml:mo lspace="0mm">∼</mml:mo></mml:math></inline-formula> 59 %) in summer. These deviations may stem from differences in the horizontal resolution of meteorological data and the time periods under investigation. Another potential source of discrepancy might be the increase in folding frequency over recent decades <xref ref-type="bibr" rid="bib1.bibx24 bib1.bibx27" id="paren.32"/>. However, only a slight upward trend in tropopause folding events (<inline-formula><mml:math id="M52" display="inline"><mml:mo lspace="0mm">&lt;</mml:mo></mml:math></inline-formula> 1 % per decade) was reported, and this magnitude of increase remains insufficient to account for the observed differences between our results and previous findings. Therefore, the recent rise in folding frequency is unlikely to be the primary driver of these discrepancies.</p>

      <fig id="F2" specific-use="star"><label>Figure 2</label><caption><p id="d2e982">Seasonal averaged tropopause folding frequencies (%, sum of shallow, medium, and deep events) from 2014 to 2023 for <bold>(a)</bold> winter, <bold>(b)</bold> spring, <bold>(c)</bold> summer, and <bold>(d)</bold> autumn. Contour lines show 250 hPa wind speed (m s<sup>−1</sup>) from the ERA5 dataset.</p></caption>
          <graphic xlink:href="https://essd.copernicus.org/articles/18/2689/2026/essd-18-2689-2026-f02.jpg"/>

        </fig>

      <p id="d2e1015">We also compared our dataset's results for different types of folds (i.e., <italic>sfold, mfold</italic> and <italic>dfold</italic>) with those reported in previous global studies <xref ref-type="bibr" rid="bib1.bibx3 bib1.bibx34 bib1.bibx36" id="paren.33"/>. The spatial distributions of the three types of folds  provided by our dataset in summer and winter are presented in Fig. <xref ref-type="fig" rid="F3"/>. Results for spring and autumn can be found in Fig. S1 in the Supplement. In general, these results are in good agreement with those reported by <xref ref-type="bibr" rid="bib1.bibx34" id="text.34"/> using ERA-Interim data (1979–2012, 1° <inline-formula><mml:math id="M54" display="inline"><mml:mo>×</mml:mo></mml:math></inline-formula> 1°) and <xref ref-type="bibr" rid="bib1.bibx3" id="text.35"/> using MERRA-2 data (2003–2018, 0.5° <inline-formula><mml:math id="M55" display="inline"><mml:mo>×</mml:mo></mml:math></inline-formula> 0.625°). During spring (Fig. S1a), shallow folds predominantly occur between 20 and 30° N, with a peak frequency observed over northern India. In summer (Fig. <xref ref-type="fig" rid="F3"/>a), the subtropical jet stream shifts northward,  accompanied by a concurrent northward movement of high-frequency shallow fold regions. The maximum frequency center is situated over the Middle East, with values exceeding 16 %. During autumn (Fig. S1b) and winter (Fig. <xref ref-type="fig" rid="F3"/>b), a southward shift of the subtropical jet stream coincides with a corresponding equatorward displacement in the spatial distribution of shallow folds. In winter, the primary <italic>sfold</italic> zone shifts back to the 20–30° N latitude band (Fig. <xref ref-type="fig" rid="F3"/>b), where the highest frequency, approximately 17 %, is observed over eastern China. The correlation between the high-frequency <italic>sfold</italic> regions and the locations of the subtropical jet stream in all seasons indicates the role of the subtropical jet stream in forming the shallow folds <xref ref-type="bibr" rid="bib1.bibx3 bib1.bibx5 bib1.bibx37" id="paren.36"/>. Regarding medium folds (<italic>mfold</italic>), their occurrence over China is predominantly observed in summer and  winter, while they are rare during spring and autumn. Specifically, in summer (Fig. <xref ref-type="fig" rid="F3"/>c), the primary region of <italic>mfold</italic> is located in Central Asia, with the maximum frequency reaching 1.6 %. The highest occurrence frequency of <italic>mfold</italic> was found in winter (Fig. <xref ref-type="fig" rid="F3"/>d), mainly in the TP and the Sichuan Basin (SCB), where the maximum frequency reaches 1.8 %. In contrast, spring (Fig. S1c) and autumn (Fig. S1d) exhibit markedly lower <italic>mfold</italic> frequencies compared to winter. In spring, the primary occurrence area is centered on the vicinity of the Himalayas <xref ref-type="bibr" rid="bib1.bibx24 bib1.bibx25" id="paren.37"/>. Notably, during winter, in high-wind-speed regions like 25–32° N, 110–140° E, the spatial distributions of <italic>mfold</italic> (Fig. <xref ref-type="fig" rid="F3"/>d) closely resemble those of <italic>sfold</italic> (Fig. <xref ref-type="fig" rid="F3"/>b), indicating that high wind speed might be a common driving factor for the formation of these two types of folds  <xref ref-type="bibr" rid="bib1.bibx3 bib1.bibx34" id="paren.38"/>. With respect to deep folding events, compared with the other two types of folds, they possess the lowest frequency. Specifically, <italic>dfold</italic> occurs least frequently in summer (Fig. <xref ref-type="fig" rid="F3"/>e), and most frequently in winter (Fig. <xref ref-type="fig" rid="F3"/>f), with the maximum frequency reaching only 0.2 %. Due to the high spatial (0.25°) and temporal (hourly) resolutions of our dataset, we were able to capture the details of folding spatial distributions that were mostly smoothed out in previous studies using low-resolution data. For instance,  the differences in <italic>sfold</italic> between eastern and western China can be more clearly distinguished in our dataset (see Fig. <xref ref-type="fig" rid="F3"/>b).  A clearer elucidation of the characteristics of <italic>mfold</italic> in the Himalayas (Fig. <xref ref-type="fig" rid="F3"/>d) and <italic>dfold</italic> in northeastern China (Fig. <xref ref-type="fig" rid="F3"/>f) was also given by our dataset.</p>

      <fig id="F3" specific-use="star"><label>Figure 3</label><caption><p id="d2e1127">Seasonal averaged frequencies (%) of <bold>(a, b)</bold> shallow, <bold>(c, d)</bold> medium, and <bold>(e, f)</bold> deep tropopause folds for summer (left) and winter (right) from 2014–2023. Contour lines show 250 hPa wind speed (m s<sup>−1</sup>) from the ERA5 dataset.</p></caption>
          <graphic xlink:href="https://essd.copernicus.org/articles/18/2689/2026/essd-18-2689-2026-f03.jpg"/>

        </fig>

      <fig id="F4" specific-use="star"><label>Figure 4</label><caption><p id="d2e1159">Comparison of monthly averaged tropopause folding frequencies (sum of shallow, medium, and deep events) between our dataset (2014–2023, <inline-formula><mml:math id="M57" display="inline"><mml:mi>y</mml:mi></mml:math></inline-formula> axis) and the ETH Zürich  dataset (1980–2014, <inline-formula><mml:math id="M58" display="inline"><mml:mi>x</mml:mi></mml:math></inline-formula> axis) for <bold>(a)</bold> winter, <bold>(b)</bold> spring, <bold>(c)</bold> summer, and <bold>(d)</bold> autumn. The black dashed line is the <inline-formula><mml:math id="M59" display="inline"><mml:mrow><mml:mn mathvariant="normal">1</mml:mn><mml:mo>:</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:math></inline-formula> line. A random 50 % subset of points is displayed for a better visualization.</p></caption>
          <graphic xlink:href="https://essd.copernicus.org/articles/18/2689/2026/essd-18-2689-2026-f04.png"/>

        </fig>

      <p id="d2e1207">We continued to utilize the monthly folding dataset from ETH Zürich  (<uri>http://eraiclim.ethz.ch/prot/folds.html</uri>, last access: 21 September 2025) spanning 1980–2014 to quantitatively validate our dataset (Fig. <xref ref-type="fig" rid="F4"/>). It can be seen that these two datasets exhibit a strong spatial correlation. A maximum correlation of 0.95 was observed in spring (Fig. <xref ref-type="fig" rid="F4"/>b), the season with the highest folding frequency. The other seasons also showed strong correlations, with coefficients all exceeding 0.9, confirming the high spatial agreement between these two datasets. However, Fig. <xref ref-type="fig" rid="F4"/> also shows that most of the data points from our dataset lie below the <inline-formula><mml:math id="M60" display="inline"><mml:mrow><mml:mn mathvariant="normal">1</mml:mn><mml:mo>:</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:math></inline-formula> line, indicating our dataset consistently reports lower folding frequencies than the ETH dataset. Specifically, the fitted line for December had the minimum slope of 0.62, indicating that our dataset predicts substantially lower frequencies than the ETH dataset during  that month. This result contrasts with our previous findings showing that our dataset detects more folds than that of <xref ref-type="bibr" rid="bib1.bibx24" id="text.39"/>. This discrepancy is likely attributable to the different time periods and the different resolutions of the meteorological data used in these studies. The ETH dataset was derived from the 1° ERA-Interim reanalysis data with 60 model levels.  The higher vertical resolution of the reanalysis data underlying the ETH dataset likely explains its higher folding frequencies compared to our results derived from data with 37 pressure levels. However, our dataset features higher temporal (1 h) and horizontal (0.25°) resolutions. In contrast, the dataset of <xref ref-type="bibr" rid="bib1.bibx24" id="text.40"/> uses the 1° ERA5 reanalysis data with 37 pressure levels.   As a result, although the vertical resolution is the same, the folding frequency reported by <xref ref-type="bibr" rid="bib1.bibx24" id="text.41"/> is lower than ours, likely  due to the coarser horizontal resolution of their reanalysis data.</p>
</sec>
<sec id="Ch1.S3.SS2">
  <label>3.2</label><title>Validation of the Dataset by Case Studies</title>
      <p id="d2e1249">Apart from comparing our dataset's results with those from global studies, we adopted three existing case studies on tropopause folding events at the monthly, daily, and hourly timescales to further verify the reliability of our dataset.</p>
<sec id="Ch1.S3.SS2.SSS1">
  <label>3.2.1</label><title>Monthly Changes in Tropopause Folds on the Tibetan Plateau in 2017</title>
      <p id="d2e1259">The Tibetan Plateau (TP), known as the “Roof of the World”, exerts a strong influence on the atmospheric circulation due to its distinctive topographic features <xref ref-type="bibr" rid="bib1.bibx42 bib1.bibx30" id="paren.42"/>. It is also a key region for deep STT <xref ref-type="bibr" rid="bib1.bibx36 bib1.bibx33 bib1.bibx34" id="paren.43"/>. <xref ref-type="bibr" rid="bib1.bibx43" id="text.44"/> presented the monthly averaged frequency of total tropopause folds over the TP from April to July 2017. We thus used these results in the monthly scale to validate our dataset.</p>
      <p id="d2e1272">Figure <xref ref-type="fig" rid="F5"/> shows the folding frequency (sum of shallow, medium, and deep events) distributions from April to July 2017, derived from our dataset. They align closely with those reported by <xref ref-type="bibr" rid="bib1.bibx43" id="text.45"/>. In April (Fig. <xref ref-type="fig" rid="F5"/>a), folding events predominantly occurred between 25 and 30° N, adjacent to the westerly jet. The primary distribution regions were found to be the northwestern part of South Asia and the southern and central TP. In May (Fig. <xref ref-type="fig" rid="F5"/>b), the folds shifted northward simultaneously with the westerly jet, concentrating near 30° N. This northward migration continued in June (Fig. <xref ref-type="fig" rid="F5"/>c), as high-frequency folding regions moved to 32–38° N. In July (Fig. <xref ref-type="fig" rid="F5"/>d), the primary folding zone shifted further north to 35–45° N. The movement of the high folding frequency zones matched the northward shift of the westerly jet during this period, suggesting that the tropopause folding displacement over the TP might be regulated by the movement of the westerly jet <xref ref-type="bibr" rid="bib1.bibx25 bib1.bibx10" id="paren.46"/>.</p>

      <fig id="F5" specific-use="star"><label>Figure 5</label><caption><p id="d2e1294">Monthly averaged tropopause folding frequencies (%, sum of shallow, medium, and deep events) on the TP in 2017 for <bold>(a)</bold> April, <bold>(b)</bold> May, <bold>(c)</bold> June, and <bold>(d)</bold> July. Contour lines show 250 hPa wind speed (m s<sup>−1</sup>).</p></caption>
            <graphic xlink:href="https://essd.copernicus.org/articles/18/2689/2026/essd-18-2689-2026-f05.png"/>

          </fig>

      <p id="d2e1328">After the comparison,  we continued to investigate the properties of total <italic>fold, sfold, mfold</italic> and <italic>dfold</italic> on the TP. Note that the total <italic>fold</italic> here encompasses all folding events, including shallow, medium, and deep folds, as well as very shallow folding events with <inline-formula><mml:math id="M62" display="inline"><mml:mrow><mml:mi mathvariant="normal">Δ</mml:mi><mml:mi>p</mml:mi><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">50</mml:mn></mml:mrow></mml:math></inline-formula> hPa. Figure <xref ref-type="fig" rid="F6"/> shows the monthly variations of the folding frequencies for the year 2017 and  the whole 10-year period (2014–2023), respectively. It is seen in Fig. <xref ref-type="fig" rid="F6"/>a that the frequency of the total <italic>fold</italic> in 2017 reaches the highest in spring and the lowest in summer. Because the frequency of the total <italic>fold</italic> is primarily determined by <italic>sfold</italic>, which accounts for 48%, the monthly variation of <italic>sfold</italic> is similar to that of the total <italic>fold</italic>. In contrast, the monthly variations of <italic>mfold</italic> and <italic>dfold</italic> are significantly different from those of <italic>fold</italic> and <italic>sfold</italic>. In 2017, <italic>mfold</italic> peaks in January and October, and <italic>dfold</italic> occurs exclusively in time periods of January–March and December. In terms of the 10-year's average (Fig. <xref ref-type="fig" rid="F6"/>b), the total <italic>fold</italic> and <italic>sfold</italic> still show high consistency, but their peaks were found in May rather than in April. With respect to <italic>mfold</italic> and <italic>dfold</italic>, <italic>mfold</italic> still peaks in January and reaches its minimum in April. The <italic>dfold</italic> was also found to occur more frequently in winter, peaking in January, and less frequently in summer and autumn. Compared to the data for 2017, the 10-year averaged results show less fluctuations in the monthly variations especially for the total <italic>fold</italic> and <italic>sfold</italic>, because the influence of  short-term folding events is smoothed out. As a result, the long-term characteristics of folds on the TP can be displayed by our dataset, which are more representative. This is also why we proposed this multi-year high-resolution folding dataset in this study, to indicate the long-term characteristics of different types of folds over China and its surrounding regions.</p>

      <fig id="F6" specific-use="star"><label>Figure 6</label><caption><p id="d2e1423">Monthly variation in the occurrence frequency of different fold types over the TP. <bold>(a)</bold> The year 2017; <bold>(b)</bold> the 2014–2023 average. Frequencies were spatially averaged over the plateau for each month. Note that  fold here includes very shallow folding events with <inline-formula><mml:math id="M63" display="inline"><mml:mrow><mml:mi mathvariant="normal">Δ</mml:mi><mml:mi>p</mml:mi><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">50</mml:mn></mml:mrow></mml:math></inline-formula> hPa.</p></caption>
            <graphic xlink:href="https://essd.copernicus.org/articles/18/2689/2026/essd-18-2689-2026-f06.png"/>

          </fig>

</sec>
<sec id="Ch1.S3.SS2.SSS2">
  <label>3.2.2</label><title>A Stratospheric Intrusion Event over the North China Plain in May 2019</title>
      <p id="d2e1460">We next examined the folding results during a stratospheric intrusion event, which is a transient manifestation of STT, over the North China Plain (NCP), as reported by <xref ref-type="bibr" rid="bib1.bibx26" id="text.47"/>. This event occurred on 19–20 May 2019 (Beijing Time, BJT) and was suggested to be driven by the Northeast Cold Vortex (NECV), a typical cold low vortex system in East Asia <xref ref-type="bibr" rid="bib1.bibx41 bib1.bibx21" id="paren.48"/>. Because tropopause fold was suggested to be the major mechanism driving the stratospheric intrusion, we used this event to further demonstrate the reliability of our dataset and investigate the association between the tropopause folds and meteorological conditions.</p>
      <p id="d2e1469">In the study of <xref ref-type="bibr" rid="bib1.bibx26" id="text.49"/>, a substantial downward folding of the tropopause  over the NCP on 19–20 May  2019  was identified, reaching a depth of 500–600 hPa. It denotes the occurrence of deep folds  during this stratospheric intrusion event. Therefore, we present the  distributions of <italic>mfold</italic> and <italic>dfold</italic> frequencies over the NCP for these two days (Fig. <xref ref-type="fig" rid="F7"/>). In general, we found notable medium and deep folding events occurring on the NCP during this intrusion period.   The locations of high folding frequency regions shown by our dataset are in good consistency with the locations of the tropopause folds reported by <xref ref-type="bibr" rid="bib1.bibx26" id="text.50"/>. On 19 May (Fig. <xref ref-type="fig" rid="F7"/>a–b), medium and deep folds were concentrated primarily between 39–42° N and 110–120° E.  Compared to <italic>mfold</italic> (Fig. <xref ref-type="fig" rid="F7"/>a), <italic>dfold</italic> exhibited a distribution shifted further east and south (Fig. <xref ref-type="fig" rid="F7"/>b), with its center located at 39° N. On 20 May (Fig. <xref ref-type="fig" rid="F7"/>c–d), the spatial extent of the folds contracted, and the distributions of both types shifted southeastward, concentrating within 37–39° N and 116–120° E. The successful detection of both medium and deep folds during this stratospheric intrusion event demonstrates our dataset's capability to accurately capture these phenomena. This, in turn, enables in-depth analysis of the stratosphere-troposphere transport.</p>

      <fig id="F7" specific-use="star"><label>Figure 7</label><caption><p id="d2e1503">Spatial distributions of <bold>(a, c)</bold> medium and <bold>(b, d)</bold> deep tropopause folds over the NCP on <bold>(a, b)</bold> 19 May  and <bold>(c, d)</bold> 20 May  2019 (BJT). The red box outlines the NCP.</p></caption>
            <graphic xlink:href="https://essd.copernicus.org/articles/18/2689/2026/essd-18-2689-2026-f07.png"/>

          </fig>

      <p id="d2e1525">After comparing the distributions of tropopause folds, we next conducted a deeper analysis of this case based on our dataset. Figures S2 (02:00–12:00, 19 May) and <xref ref-type="fig" rid="F8"/> (14:00, 19 May–10:00, 20 May) show the distributions of <italic>mfold</italic> and <italic>dfold</italic> as well as the upper-level weather patterns  (200 hPa) over the NCP.  It was found in Fig. S2  that between 02:00 and 12:00 on 19 May, a trough gradually migrated eastward, and the folding region also shifted correspondingly from the western to the northern NCP. At 14:00 (Fig. <xref ref-type="fig" rid="F8"/>a), the trough had moved to the northern NCP, where <italic>mfold</italic> were found. Afterward, the trough continued its eastward movement (Fig. <xref ref-type="fig" rid="F8"/>b–c), and <italic>dfold</italic> were subsequently formed in the northern NCP accordingly. On 20 May   (Fig. <xref ref-type="fig" rid="F8"/>d–f), both the trough and the folding regions further moved eastward and gradually departed from the NCP. Based on the complete evolution of the event, we found that the vast majority of medium and deep folds initially formed on the western flank of the trough, and the folding region shifted eastward with the eastward movement of the trough. Thus, we identified the trough as the primary driving factor of medium and deep tropopause folds during this event. This mechanism is also consistent with the conclusion achieved by <xref ref-type="bibr" rid="bib1.bibx26" id="text.51"/> and is attributed to the strong downdrafts formed near the trough, which can enhance the fold formation <xref ref-type="bibr" rid="bib1.bibx26 bib1.bibx46 bib1.bibx19" id="paren.52"/>. Regarding the small portion of <italic>dfold</italic> on the eastern flank of the trough (e.g., Fig. <xref ref-type="fig" rid="F8"/>d), we attribute this feature to the eastward advection of folds after their formation, driven by strong upper-level westerly winds. Due to the high temporal resolution and extensive temporal coverage of our dataset, the complete evolution of this event including its details can be successfully captured and analyzed.</p>

      <fig id="F8" specific-use="star"><label>Figure 8</label><caption><p id="d2e1563">Spatiotemporal evolution of medium and deep tropopause folds over the NCP on <bold>(a–c)</bold> 19 May 19 and <bold>(d–f)</bold> 20 May  2019 (BJT), shown at 4 h intervals. Colors indicate fold type (green: medium, red: deep). Wind vectors (arrows) show 200 hPa wind speed and direction, scaled by color and length. Brown lines represent the upper-level trough, and the red box delineates the NCP. Gray solid lines indicate contours of the 200 hPa geopotential height (unit: dagpm).</p></caption>
            <graphic xlink:href="https://essd.copernicus.org/articles/18/2689/2026/essd-18-2689-2026-f08.png"/>

          </fig>

</sec>
<sec id="Ch1.S3.SS2.SSS3">
  <label>3.2.3</label><title>A Stratospheric Intrusion Event over the Tibetan Plateau and the Sichuan Basin in February 2016</title>
      <p id="d2e1586"><xref ref-type="bibr" rid="bib1.bibx45" id="text.53"/> reported the occurrence of a stratospheric intrusion event on the TP and the Sichuan Basin (SCB) during 11–12 February  2016 (BJT) and they suggested it as the primary cause of the anomalous ozone surge in this region.  This event serves as a critical case study for short-duration stratospheric intrusions, highlighting their capacity to rapidly and substantially impact surface air quality in southwestern China. Consequently,  we investigated the distributions of tropopause folds during this period using our dataset.</p>
      <p id="d2e1591">In <xref ref-type="bibr" rid="bib1.bibx45" id="text.54"/>'s study, they reported that between 11–12 February, within the region 90–100° E, 26–30° N, the tropopause underwent noticeable folding and  descended to the top of the  boundary layer. Thus, we  present the distributions of three fold types over the TP and the SCB on 11 February (Fig. S3) and 12 February (Fig. <xref ref-type="fig" rid="F9"/>) from our dataset. On 11 February (Fig. S3), widespread shallow folds occurred across the eastern TP and the SCB within 26–32° N. Simultaneously, medium folds were formed in the southeastern corner of the TP and along the southern flank of the SCB. On 12 February (Fig. <xref ref-type="fig" rid="F9"/>), the spatial extent of the shallow folds had diminished, compared with that on 11 February. Between 14:00 (Fig. <xref ref-type="fig" rid="F9"/>a) and 20:00 (Fig. <xref ref-type="fig" rid="F9"/>d), shallow folds were primarily concentrated between 28 and 31° N. In contrast, the spatial extent of medium folds increased on 12 February (see regions shaded in green in Fig. <xref ref-type="fig" rid="F9"/>) relative to its extent on 11 February. These two folds predominantly occurred across the southern flank of the SCB and adjacent areas. Scattered medium folds were also observed near 28° N along the eastern periphery of the TP. With respect to the deep folds, they were found to emerge mainly to the south of the SCB at 18:00 (see regions shaded in red in  Fig. <xref ref-type="fig" rid="F9"/>c). Subsequently, the deep folds propagated eastward along with the medium folds, and the center of the deep folds reached 108° E by 20:00 (see Fig. <xref ref-type="fig" rid="F9"/>d). The distributions of these tropopause folds at different times revealed by our dataset are also in good agreement with the locations reported by <xref ref-type="bibr" rid="bib1.bibx45" id="text.55"/>. This correspondence further confirms that our dataset can successfully capture the folding events associated with the stratospheric intrusion.</p>

      <fig id="F9" specific-use="star"><label>Figure 9</label><caption><p id="d2e1617">Distribution of shallow (blue), medium (green), and deep (red) tropopause folds over the SCB and TP on 12 February, 2016. <bold>(a–d)</bold> 14:00, 16:00, 18:00, and 20:00 BJT. The SCB and TP are outlined by blue and red boxes, respectively.</p></caption>
            <graphic xlink:href="https://essd.copernicus.org/articles/18/2689/2026/essd-18-2689-2026-f09.png"/>

          </fig>

</sec>
</sec>
</sec>
<sec id="Ch1.S4">
  <label>4</label><title>Data availability</title>
      <p id="d2e1639">The dataset of tropopause folds generated in this study is available in the Zenodo repository (<ext-link xlink:href="https://doi.org/10.5281/zenodo.17446871" ext-link-type="DOI">10.5281/zenodo.17446871</ext-link>, <xref ref-type="bibr" rid="bib1.bibx9" id="altparen.56"/>).</p>
</sec>
<sec id="Ch1.S5" sec-type="conclusions">
  <label>5</label><title>Conclusions and Future Work</title>
      <p id="d2e1657">In this study, we developed a high-resolution tropopause folding dataset for China and its surrounding regions using the ERA5 meteorological reanalysis data and a three-dimensional labeling method. The dataset spans a decade from 2014 to 2023, covering a spatial domain of 50–140° E and 0–70° N with a horizontal resolution of 0.25°  and an hourly temporal resolution.</p>
      <p id="d2e1660">To validate the dataset's accuracy, we conducted a comprehensive assessment from two perspectives: overall climatological characteristics and individual case studies. In terms of overall characteristics, we compared the spatial distributions and occurrence frequencies of three fold types (shallow, medium, and deep) with those reported in previous global studies <xref ref-type="bibr" rid="bib1.bibx24 bib1.bibx7 bib1.bibx3 bib1.bibx34 bib1.bibx36" id="paren.57"/>. The results showed that the distribution patterns of folds in our dataset are highly consistent with those in previous studies. Furthermore, owing to its high resolution, our dataset can more clearly resolve the detailed features of fold distributions and identify regions with high folding frequencies. For the case studies, we analyzed three events spanning monthly, daily, and hourly timescales <xref ref-type="bibr" rid="bib1.bibx43 bib1.bibx26 bib1.bibx45" id="paren.58"/>, and all the results provided by our dataset demonstrated a high degree of consistency with the previous researches. These case studies also confirm that our dataset can reliably capture the detailed evolution of tropopause folds during the stratospheric intrusion events, providing a solid foundation for subsequent process studies of these events. Thus, the dataset proposed in this study exhibits high precision and reliability, so that it enables in-depth studies of tropopause folding events and facilitates a better understanding of the mechanisms underlying the stratospheric intrusion.</p>
      <p id="d2e1669">Aside from these advantages, the current dataset still has some limitations. Future work should focus on expanding the temporal and spatial coverage to encompass longer periods and broader regions. Additionally, we plan to integrate this dataset with data of atmospheric constituent concentrations and weather systems. This would enable a more comprehensive investigation of the impacts of tropopause folds on atmospheric composition, air quality, and regional weather systems, thereby further enhancing the scientific value of this resource.</p>
</sec>

      
      </body>
    <back><app-group>
        <supplementary-material position="anchor"><p id="d2e1671">The supplement related to this article is available online at <inline-supplementary-material xlink:href="https://doi.org/10.5194/essd-18-2689-2026-supplement" xlink:title="pdf">https://doi.org/10.5194/essd-18-2689-2026-supplement</inline-supplementary-material>.</p></supplementary-material>
        </app-group><notes notes-type="authorcontribution"><title>Author contributions</title>

      <p id="d2e1682">LC conceptualized the study and supervised the entire research process. YY conducted the simulations and processed the data. LX, MW, QY and YZ contributed to the interpretation of the results. TZ,  XL and JM assisted with the data analysis and manuscript preparation. TZ provided valuable insights into the model results.  All authors discussed the results and contributed to the final manuscript.</p>
  </notes><notes notes-type="competinginterests"><title>Competing interests</title>

      <p id="d2e1688">The contact author has declared that none of the authors has any competing interests.</p>
  </notes><notes notes-type="disclaimer"><title>Disclaimer</title>

      <p id="d2e1694">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. The authors bear the ultimate responsibility for providing appropriate place names. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.</p>
  </notes><ack><title>Acknowledgements</title><p id="d2e1700">The authors would like to thank the National Supercomputer Center in Tianjin and High Performance Computing Center at the Nanjing University of Information Science and Technology to provide the high-performance computing system for calculations.</p></ack><notes notes-type="financialsupport"><title>Financial support</title>

      <p id="d2e1705">This study is funded by the National Key Research and Development Program of China (grant no. 2022YFC3701204), the National Natural Science Foundation of China (grant no. 41705103), and the 2023 Outstanding Young Backbone Teacher of Jiangsu “Qinglan” Project (grant no. R2023Q02).</p>
  </notes><notes notes-type="reviewstatement"><title>Review statement</title>

      <p id="d2e1711">This paper was edited by Graciela Raga and reviewed by three anonymous referees.</p>
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