Climate variability is a main driver of mass imbalance of the Greenland Ice Sheet. However, quantitative assessments of climate change in this region are constrained by the sparseness, heterogeneity, and discontinuity of in situ measurements, imposing significant challenges to reliable modelling and future projections. To address this, we compile all available weather station observations over Greenland from 1941 to present into a single and standardized dataset at both daily and monthly temporal resolutions. This dataset includes seven climate variables: temperature, wind speed, wind direction, relative humidity, radiation, precipitation, and pressure, which is accessible at <a href="https://doi.org/10.11888/Atmos.tpdc.303490" target="_blank" rel="noopener">https://doi.org/10.11888/Atmos.tpdc.303490</a> (Wang and Wang, 2026). All data were harmonized and processed by a uniform quality control procedure, with the outliers flagged. For each station, detailed metadata covering source, location, and data availability are supplied. The dataset facilitates a thorough analysis of multi-variable climate characteristics in Greenland and can be used for the validation of global reanalysis products, regional climate models and remote sensing retrievals as well as data assimilation and climate reconstruction. Furthermore, we use the temperature observations from the compiled dataset together with a digital elevation model and ERA5 reanalysis to reconstruct a high-resolution (0.1°) monthly surface air temperature (SAT) dataset for Greenland (1950–2024), based on the optimized method identified after comparing five distinct machine learning models. The resulting SAT reconstruction exhibit a great improvement over ERA5, reducing the root mean square error (RMSE) by more than 60 % and increasing squared correlation coefficient (<em>R</em><sup>2</sup>) by 5 %. The high accuracy of this reconstruction is further confirmed by spatial cross-validation across 11 distinct regional divisions. The reconstruction is also publicly available at the Third Pole Environment Data Center (<a href="https://doi.org/10.11888/Cryos.tpdc.303026" target="_blank" rel="noopener">https://doi.org/10.11888/Cryos.tpdc.303026</a>, Wang and Wang, 2025).