Advancing weather index insurance for climate risk management: A review of modelling techniques and implementation strategies

Weather index insurance (WII) is a promising climate risk management tool, offering a robust mechanism to enhance agricultural resilience and mitigate the impacts of weather extremes driven by increasing climate variability. The continuous evolution of computational modelling techniques presents significant opportunities to improve the development, accuracy, and reliability of WII schemes. This systematic review, conducted following PRISMA guidelines, meticulously analyzes 87 peer-reviewed studies (2008–2025). The primary focus of the study is on advanced modelling approaches for WII design, evaluation, and optimization, along with an in-depth examination of data sources and their integration. The review categorizes modelling techniques into traditional statistical methods and advanced machine learning and deep learning, highlighting their roles in hazard identification, vulnerability assessment, and insurance pricing. Furthermore, emerging technologies like blockchain and the Internet of Things (IoT) are explored for their potential to support transparent, automated, and scalable insurance delivery. Special attention is given to integrating multi-source climate data (ground-based, gridded, satellite) and addressing critical challenges such as basis risk, model validation, and spatiotemporal alignment. We identify 49 unique indices for quantifying climate indicators across various hazards and evaluate modelling frameworks for capturing complex climate-agriculture interactions. The study provides a comprehensive roadmap by reviewing modelling innovations, data integration practices, index design strategies, and policy frameworks for strengthening WII as a climate adaptation mechanism, supporting sustainability indicators aligned with global resilience goals for vulnerable agricultural systems facing rising climate risks.