<p>This paper investigates the pivotal role of expert elicitation (EE) in shaping Decision Support Systems (DSS) for climate change-related decision-making, with a particular focus on the need for transparent standards and robust practices as demand for reliable climate services increases. By systematically reviewing 36 peer-reviewed studies from the SCOPUS database, we find that the reporting of EE methods in climate DSS development is highly variable, with less than 40% of studies providing sufficient detail on expert selection criteria or elicitation protocols. Methodological transparency, including the documentation of elicitation processes and integration of expert input with DSS design and output, is often lacking. Despite these gaps, we identify a subset of best practices, such as consideration of bias and reporting of challenges in expert elicitation which foster trust and improve the contextual relevance of DSS outputs. Our review also reveals that, while artificial intelligence (AI) is increasingly used for processing complex climate datasets, the integration of expert knowledge into hybrid collective intelligence (HyCI) tools remains rare—only one study in our search explicitly combined these approaches for climate DSS development. Building on existing frameworks, we propose a structured pathway for embedding rigorous and transparent EE processes into next-generation DSS. Our findings underscore the urgent need for standardisation in EE reporting and highlight actionable recommendations to enhance the societal relevance, trustworthiness, and impact of climate services powered by both human expertise and AI.</p>