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Understanding climate change relies on sustained observations of Essential Climate Variables (ECVs), as defined by the Global Climate Observing System (GCOS). As access to ECVs has expanded in scope and duration, users are increasingly confronted with the complexity of these datasets, including longer time series, different data structures, multiple product versions, and uncertainty estimates. To remove common technical barriers, such as installing software and coding libraries or, locating and downloading large datasets, the European Space Agency’s Climate Change Initiative (ESA-CCI) developed a cloud-based, pre-configured JupyterLab environment designed to allow learners to begin working with satellite-derived ESA-CCI climate data within minutes. This pre-configured JupyterLab environment supports users by integrating simplified access to decades-long global records of the 27 satellite-derived ESA-CCI ECVs into the ESA CCI Toolbox, a dedicated Python package specifically designed for ESA-CCI data that provides ready-to-use functions, allowing users to focus on visualising and analysing climate signals rather than writing custom code from scratch. We present this environment as the foundation for a series of training events that have successfully engaged diverse audiences, including students, early-career researchers, and non-specialist stakeholders1. Through guided notebooks that walk learners through accessing ESA-CCI data, filtering and aggregating variables, visualising spatial and temporal patterns, and exploring uncertainties and data quality flags, learners gain hands-on, reproducible climate data analysis experience while deepening their understanding of the significance of satellite-derived ECVs and their role in monitoring and interpreting climate change. Our presentation will give the opportunity for conference participants to explore the JupyterLab environment during the PICO session. 1 https://climate.esa.int/en/climate-change-initiative-training/training-sessions/