New <em>in situ</em>, interpolated hydrographic monthly climatologies were created for the Northeast U.S. Continental Shelf (NES) and surrounding boundary waters for the years 2000–2024. The climatologies were created at two depth bins: the surface and bottom, where the surface is defined as observations averaged above the mixed layer depth, and the bottom includes all averaged data within 10 meters of the bathymetric bottom. The products described here rely solely on <em>in situ</em> measurements rather than satellite imagery, as satellite-derived products can be coarse and only reflect the skin layer of the ocean. Multiple point data sources and data collection methods, including observations collected from traditional vertical oceanographic profiles, flowthrough systems, buoys, gliders, and others, were aggregated to create the <em> in situ</em> dataset. Spatiotemporal Barnes objective analysis was performed to create the monthly climatologies for both temperature and salinity at the two depth bins. Root mean squared error (RMSE) and an uncertainty metric were provided with the interpolation. The uncertainty metric highlights regions and seasons that are data-poor and/or highly variable, and where there is lower confidence in the interpolated values. Results were validated using reference climatologies created using Global Ocean Reanalysis and Simulations (GLORYS) modeled data with the same spatiotemporal bounds. The surface temperature and salinity climatologies both yield significant correlations when compared to the reference climatologies (bias<|1.068| °C). Lower correlations in the winter months are observed during validation of bottom temperature climatologies, due to seasonal sampling bias. The products described here provide high-resolution, updated monthly climatologies derived from <em>in situ</em> data that may be used for anomaly detection or other modeling efforts. The climatologies can be accessed at doi:<a href="https://doi.org/10.5281/zenodo.20183495">10.5281/zenodo.20183494</a> (Synan et al., 2026a) and the data used to create them can be accessed at doi:<a href="https://doi.org/10.5281/zenodo.22696811">10.5281/zenodo.22696811</a> (Synan et al., 2026b).