Description: Climate data come from a variety of sources, including remote weather stations, satellite platforms, and earth system models. Accurate temperature, precipitation, soil moisture, and other climate data are critical for many professions, including urban planning, resource management, agriculture, and energy production systems. However, the increasing diversity, complexity, and size of these datasets can make them difficult to work with. Creating decision-ready products from raw climate data also requires transparent, reproducible workflows that can be updated as requirements change. In this workshop, based in the Python programming language, we introduce participants to NASA’s free-to-use climate datasets, demonstrating how to search for, access, and manipulate satellite-based and modeled data on air temperature, precipitation, soil moisture, and humidity, among many potential climate variables. Participants will be introduced to hierarchical data formats, including HDF5 and netCDF4, along with the xarray library in Python. Our workshop offers hands-on experience with subsetting, resampling, and visualizing spatial data cubes: gridded climate variables that vary over space and time.