Ground-based microwave radiometers (MWRs) are indispensable instruments for the continuous observation of atmospheric temperature and humidity profiles. The reliability of brightness temperature (TB) measurements and the accuracy of retrieved atmospheric profiles are fundamental to their effective use in both research and operational applications. In this study, we present a long-term dataset of multi-channel microwave brightness temperature observations and corresponding retrieved atmospheric profiles derived from the RPG-HATPRO MWR deployed at the Xianghe Integrated Observatory (XH) in Hebei Province, China, covering the period 2013–2022. Minute-level TB observations over the 10-year period were integrated with collocated infrared cloud detection data to establish a comprehensive dataset featuring a detailed weather-type classification. The quality of the observed TBs was carefully evaluated using a radiative transfer model. The results demonstrate excellent agreement between simulated and observed multi-channel TBs, with correlation coefficients typically exceeding 0.96 and mean biases within 1 K, confirming the stable and reliable performance of the XH MWR throughout the entire observation period. Based on the quality-controlled TBs, two retrieval schemes for atmospheric temperature and humidity profiles were developed using collocated radiosonde observations and ERA5 reanalysis data. For clear-sky conditions, an optimal estimation (OE)-based retrieval model was employed, whereas a deep neural network (DNN)-based model was designed for cloudy-sky retrievals. Validation against radiosonde measurements shows that both retrieval schemes achieved substantially improved accuracy up to 35 % for temperature and 25 % for humidity profiles by compared with the MWR’s self-developed products. Combining the two retrieval models with the 10-year quality-controlled TB dataset, we constructed a comprehensive data record characterized by decadal-scale, high temporal resolution (1~10 min), and integrated MWR observations and profiles dataset. Based on the long-term dataset, it reveals a weak but consistent near-surface warming trend across 100–1000 m. They also capture the frequent surface-based inversions, occurred from 6 % in summer to 68 % in winter. Critically, SBI occurrence increases with PM_2.5 levels, reaching >60 % under severe pollution (>250 µg m⁻³). Those applications demonstrate the dataset’s value for boundary layer studies, climate trend analysis, and air quality forecasting.