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<p>In situ PM<span class="inline-formula">_2.5</span> concentration observations have long been used as critical data sources in haze-related studies. Due to the frequently occurring haze pollution events, China started to regularly monitor PM<span class="inline-formula">_2.5</span> concentration nationwide from the newly established air quality monitoring network in 2013. Nevertheless, the acquisition of these invaluable air quality samples is challenging given the absence of a publicly available data download interface. In this study, we provided a homogenized in situ PM<span class="inline-formula">_2.5</span> concentration dataset that was created on the basis of hourly PM<span class="inline-formula">_2.5</span> data retrieved from the China National Environmental Monitoring Center (CNEMC) via a web crawler between 2015 and 2019. Methods involving missing value imputation, change point detection, and bias adjustment were applied sequentially to deal with data gaps and inhomogeneities in raw PM<span class="inline-formula">_2.5</span> observations. After excluding records with limited samples, a homogenized PM<span class="inline-formula">_2.5</span> concentration dataset comprising of 1309 5-year long PM<span class="inline-formula">_2.5</span> data series at a daily resolution was eventually compiled. This is the first attempt to homogenize in situ PM<span class="inline-formula">_2.5</span> observations in China. The trend estimations derived from the homogenized dataset indicate a spatially homogeneous decreasing tendency of PM<span class="inline-formula">_2.5</span> across China at a mean rate of about <span class="inline-formula">−</span>7.6&thinsp;% per year from 2015 to 2019. In contrast to raw PM<span class="inline-formula">_2.5</span> observations, the homogenized data record not only has complete data integrity but is more consistent over space and time. This homogenized daily in situ PM<span class="inline-formula">_2.5</span> concentration dataset is publicly accessible at <a href="https://doi.org/10.1594/PANGAEA.917557">https://doi.org/10.1594/PANGAEA.917557</a> (Bai et al., 2020a) and can be applied as a promising dataset for PM<span class="inline-formula">_2.5</span>-related studies such as satellite-based PM<span class="inline-formula">_2.5</span> mapping, human exposure risk assessment, and air quality management.</p>