D1.5: Second report on KPI for quality assurance of the project

About this document 2nd assessment of the implementation of project using the KPIs. The final assessment will be reported in the final technical report. Work package in charge WP1 Executive summary This final report on Key Performance Indicators (KPIs) and Quality Assurance (QA) activities documents the internal monitoring framework of the nextGEMS project (Grant Agreement No. 101003470), covering the period from September 2021 to July 2025. As a Horizon 2020 initiative focused on storm-resolving Earth system modeling, nextGEMS adopted an internal KPI system aligned with project goals to support scientific progress, stakeholder engagement, and highquality dissemination. A total of six KPIs were monitored across scientific/technical, management, impact, and dissemination dimensions. Regular internal assessments, stakeholder-informed feedback, and iterative Hackathon-based collaboration were at the core of the KPI and QA strategy. All KPIs have been achieved. This report provides an overview of the methodology used to assess the KPIs, a detailed evaluation of each indicator, and reflections on overall project performance and quality assurance mechanisms. The assessments demonstrate that all six KPIs were fully or substantially achieved. Both SR-ESM systems (IFS-FESOM and ICON) met the expected technical and physical performance standards, engagement with early-stage researchers and external stakeholders was strong, and internal collaboration and communication processes were continuously improved over the project lifetime. The quality assurance approach combined formal review of deliverables, embedded peer feedback, open data access, and external validations—including computing center reviews and peer-reviewed publications. No major risks remain open, and the KPI framework proved useful as an internal steering tool throughout the project. Lessons learned and recommendations include the value of embedding qualitative monitoring in research workflows, promoting cross-WP transparency, and integrating user feedback directly into model development and application cycles. These insights may inform future large-scale Earth system modeling initiatives and interdisciplinary climate research projects.