Irrigation water use (IWU) is the largest direct human intervention in the terrestrial water cycle, yet it remains poorly characterized at the spatial and temporal scales required for climate research. We present a long-term archive of monthly IWU estimates at 0.25° spatial resolution for four major irrigated regions — the contiguous United States (CONUS), India, the Murray–Darling Basin in Australia, and the Ebro Basin in Spain — spanning up to two decades depending on input data availability. The datasets are derived using three distinct approaches: (i) a Soil Moisture (SM)–based Delta method that infers irrigation from discrepancies between satellite and model SM and evapotranspiration, (ii) an SM–based inversion of the soil water balance constrained by satellite SM, and (iii) a model–observation integration scheme combining a land surface model with satellite-based irrigated-area maps. Across regions, these approaches yield up to six SM-based Delta products, five SM-based Inversion products, and one Model–observation integration product. Validation against available irrigation records shows that several method–input combinations reproduce the order of magnitude of annual state-level irrigation volumes in the CONUS, with typical errors for the best-performing datasets of about 4–5 km<sup>3</sup> yr<sup>−1</sup> in root mean square deviation and 1–2 km<sup>3</sup> yr<sup>−1</sup> in bias. In the Murray–Darling and Ebro basins, the products capture the main features of the seasonal irrigation cycle, with variations in spatial patterns, magnitude, and timing across methods. In India, where no observational records are available, the datasets reproduce the expected agricultural seasons while exhibiting a wider inter-method spread. This coordinated dataset collection, produced with multiple Earth Observation-based approaches and harmonized inputs across regions, provides long-term, spatially explicit IWU estimates and a basis for better quantifying and representing irrigation in large-scale hydrological and climate studies. The complete archive of datasets is freely available at <a href="https://doi.org/10.5281/zenodo.14988197" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.14988197</a> (Laluet et al., 2025b).