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Abstract Spatially-distributed time-series data support a range of environmental modeling and data research efforts. A critical first step to any such effort is acquiring interpolated hydrometeorological data. Standardized tools to facilitate this process into analyses have not been readily available for watershed scale research. Here, we introduce the Observatory for Gridded Hydrometeorology (OGH), an open source python library that fills this critical software gap by providing a cyberinfrastructure component to fetch and manage distributed data processed from regional and continental-scale gridded hydrometeorology products. Our approach involves annotating metadata to make gridded data products discoverable and usable within the software, enabling interoperability and reproducibility of models that use the data. This paper presents the design, architecture, and application of OGH using four commonly practiced use-cases with gridded time-series data at watershed scales. OGH and its associated annotations are distributed via Anaconda Cloud within conda-forge package repository. The tutorial Jupyter notebooks for each example use-case are available within the Freshwater Initiative Observatory repository (https://github.com/Freshwater-Initiative/Observatory). The examples are designed to utilize the compute resources and software libraries provided by HydroShare ((https://www.hydroshare.org/resource/87dc5742cf164126a11ff45c3307fd9d)). Highlights • OGH initializes data retrieval using the geospatial boundary defined by an ESRI shapefile. • OGH facilitates intercomparison of multiple gridded data products within a user-defined extent. • OGH provides a metadata template for annotation of 1D ASCII time-series gridded data products. • OGH is designed to compute geospatial-temporal analysis in distributed Docker or server environments. • OGH use cases (4) illustrate the application usability for gridded hydrometeorological data processing. [ABSTRACT FROM AUTHOR]