Abstract Agricultural monitoring, seasonal crop forecasting and climate change adaptation planning all require identifying where, when, how and which crops are grown. Global gridded cropping system data products offer useful information for these applications. However, not only the main sources of information (satellites, censuses, surveys and models) but also the spatial and temporal resolutions of these data products are quite distant from each other because of different user requirements. This is a barrier to strengthening collaborations among the research communities working to increase the capacity of societies to manage climate risks for global food systems, from extreme weather disasters to climate change. A first step is to improve cropping system data products so they can be used more seamlessly across various applications than they are currently. Toward this goal, this article reviews global gridded data products of crop variables (area, yield, cropping intensity, etc) using systematic literature survey, identifies their current limitations, and suggests directions for future research. We found that cropland or crop type mapping and yield or production estimation/prediction together accounted for half of the research objectives of the reviewed studies. Satellite-based data products are dominant at the finer resolution in space and time (<10 km and daily to annual), while model-based data products are found at the coarser resolutions (>55 km and ⩾decadal). Census-based data products are seen at intermediate resolutions (10–55 km and annual to decadal). The suggested directions for future research include the hybridization of multiple sources of information, improvements to temporal coverage and resolution, the enrichment of management variables, the exploration of new sources of information, and comprehensiveness within a single data product.