Accurate estimates of long-term surface temperature (ST) changes are fundamental not only for assessing observed warming, but also for improving the reliability of future climate projections. However, substantial missing information in global ST datasets, remains a major source of uncertainty in estimating global or regional temperature changes.&nbsp; Recent advances in artificial intelligence (AI) have promoted the effective application of deep learning approaches, such as image inpainting and transfer learning, in reconstructing incomplete geophysical datasets. In this study, partial convolutional neural network (PConv) models were trained using the 20CR reanalysis data and CMIP6 climate model outputs as training samples, with the aim of achieving a proper reconstruction of the global surface temperature dataset. To address differences among existing sea surface temperature (SST) datasets, we reconstruct global monthly ST fields since 1850 by merging the China global Land Surface Air Temperature (C-LSAT2.1) dataset with Extended Reconstructed Sea Surface Temperature (ERSSTv6) dataset and Met Office Hadley Centre's sea surface temperature (HadSST4) dataset, respectively. Although both reconstructions reliably reproduce large-scale spatial patterns and long-term variations, the merge of C-LSAT2.1 with HadSST4 exhibits greater physical consistency and is therefore adopted as our preferred reconstruction. In particular, validation against station observations indicates that the reconstructions perform well over the Antarctica after 1961, where observational coverage is extremely sparse. Based on this framework, we developed the China global Artificial Intelligence Reconstructed Surface Temperature_20CR/CMIP6 (C-AIRST_R/M) datasets, providing spatially complete global monthly ST anomaly reconstructions since 1850 with a spatial resolution of 5&deg;<span>&thinsp;</span>&times;<span>&thinsp;</span>2.5&deg;. These datasets offer improved support for extending long-term climate records and for applications in polar climate assessment, as well as in climate monitoring, detection, and attribution studies. The C-AIRST_R/M datasets can be downloaded at <a href="https://doi.org/10.6084/m9.figshare.30663797.v1" target="_blank" rel="noopener">https://doi.org/10.6084/m9.figshare.30663797.v1</a> (Ouyang et al., 2025). They are also available from <a href="http://www.gwpu.net/en/h-col-103.html" target="_blank" rel="noopener">http://www.gwpu.net/en/h-col-103.html</a> (last access: 21 November 2025).