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<p>The wet-bulb temperature (WBT; <span class="inline-formula"><i>T</i>_W</span>) comprehensively characterizes the temperature and humidity of the thermal environment and is a relevant variable to describe the energy regulation of the human body. The daily maximum <span class="inline-formula"><i>T</i>_W</span> can be effectively used in monitoring humid heat waves and their effects on health. Because meteorological stations differ in temporal resolution and are susceptible to non-climatic influences, it is difficult to provide complete and homogeneous long-term series. In this study, based on the sub-daily station-based HadISD (Met Office Hadley Centre Integrated Surface Database) dataset and integrating the NCEP-DOE reanalysis dataset, the daily maximum <span class="inline-formula"><i>T</i>_W</span> series of 1834 stations that have passed quality control were homogenized and reconstructed using the method of Climatol. These stations form a new dataset of global station-based daily maximum <span class="inline-formula"><i>T</i>_W</span> (GSDM-WBT) from 1981 to 2020. Compared with other station-based and reanalysis-based datasets of <span class="inline-formula"><i>T</i>_W</span>, the average bias was <span class="inline-formula">−</span>0.48 and 0.34 <span class="inline-formula">^∘</span>C, respectively. The GSDM-WBT dataset handles stations with many missing values and possible inhomogeneities, and also avoids the underestimation of the <span class="inline-formula"><i>T</i>_W</span> calculated from reanalysis data. The GSDM-WBT dataset can effectively support the research on global or regional extreme heat events and humid heat waves. The dataset is available at <a href="https://doi.org/10.5281/zenodo.7014332">https://doi.org/10.5281/zenodo.7014332</a> (Dong et al., 2022).</p>