We present a near surface air temperature (NSAT) fused data product over the contiguous United States using Level 2 data from the Atmospheric Infrared Sounder (AIRS), on the Aqua satellite, and the Cross-track Infrared Microwave Sounding Suite (CrIMSS), on the Suomi National Polar-orbiting Partnership (SNPP) satellite. We create the fused product using Spatial Statistical Data Fusion (SSDF), a procedure for fusing multiple datasets by modeling spatial dependence in the data, along with ground station data from NOAA’s Integrated Surface Database (ISD) which is used to estimate bias and variance in the input satellite datasets. Our fused NSAT product is produced twice daily and on a 0.25-degree latitude-longitude grid. We provide detailed validation using withheld ISD data and comparison with ERA5-Land reanalysis. The fused gridded product has no missing data; has improved accuracy and precision relative to the input satellite datasets, and comparable accuracy and precision to ERA5-Land; and includes improved uncertainty estimates. Over the domain of our study, the fused product decreases daytime bias magnitude by 1.7 K and 0.5 K, nighttime bias magnitude by 1.5 K and 0.2 K, and overall RMSE by 35% and 15% relative to the AIRS and CrIMSS input datasets, respectively. Our method is computationally fast and generalizable, capable of data fusion from multiple datasets estimating the same quantity. Finally, because our product reduces bias, it produces long-term datasets across multi-instrument remote sensing records with improved bias stationarity, even as individual missions and their data records begin and end.