We employed an algorithm for estimating the monthly average sea surface nitrate (SSN) on a global 1° by 1° resolution grid; this algorithm relies on the empirical relationship between the World Ocean Atlas 2018 (WOA18) monthly interpolated climatology of nitrate in each 1° × 1° grid and the estimated monthly sea surface temperature (SST) and photosynthetically active radiation (PAR) datasets from Moderate Resolution Imaging Spectroradiometer (MODIS) and mixed layer depth (MLD) from the Hybrid Coordinate Ocean Model (HYCOM). This dataset contains (1) the predictor variables used to construct the models; (2) the dependent variables used in model development; (3) the local multivariate linear regression models; (4) the global monthly SSN products from 2003 to 2023, generated by local multivariate linear regression models; (5) the validation dataset containing measured and model predictions for 2018-2023. The predictor variables of the method include SST, MLD and PAR. The spatial resolution of the simulated dataset is 1° by 1°. The units of SSN concentration are µmol/l. The relevant data describing paper has been published in the Journal 'Science of the Total Environment' in 2024.