Developing robust environmental and climate modeling workflows for ocean and coastal applications requires the integration of big heterogeneous data sources, numerical models, and statistical methods across multiple spatial and temporal scales. However, these complex modeling chains are often difficult to transfer, reproduce, or extend due to fragmented implementations, intricate dependencies, and limited generalizability. Here we present BlueMath, an open-source Python framework for building reproducible, efficient and scalable analysis workflows to investigate transformation processes at the ocean–coast interface. BlueMath is structured into three hierarchical levels: a Toolkit of modular core functionalities; a Methods layer for workflow construction; and a Climate Services library for application-oriented products. By standardizing building blocks and utilizing containerized environments, BlueMath enables the efficient development, testing, and deployment of end-to-end modeling chains. Special emphasis will be put in this presentation in a modular system developed for compound coastal flooding risk and adaptation in a changing climate.