<p>Weather index insurance is a financial tool that enhances climate resilience in agriculture by providing timely compensation linked to objective weather parameters. While payouts in traditional crop insurance are based on actual losses experienced by the farmer, WII payouts are triggered when a predetermined index (e.g. temperature or rainfall) exceeds a specified threshold. This review paper underscores that the primary challenge associated with WII is its susceptibility to basis risk, wherein triggered payouts may not align with actual crop yield losses. This thesis comprehensively reviews 71 quantitative studies on WII design and pricing. The review findings highlight the potential for machine learning models in optimising WII parameters, such as contract dates, strike and exit thresholds, and in developing customised multi-indexes, while also exploring the utility of phenological and remote sensing data. The main contribution of this review is a novel focus on quantitative and data driven methodologies for WII product design, pricing and evaluating hedging efficiency. Additionally, recommendations are suggested to tailor WII design and pricing to different agricultural systems and regions with spatial and temporal climate variation, enabling optimisation of risk mitigation to the agricultural communities. This review also proposes research gaps to address the multi-faceted nature of basis risk including spatial, design and temporal basis risk. With the global demand for WII on the rise, these efforts are pivotal in ensuring that WII evolves into a dependable risk management tool capable of safeguarding against extreme crop yield losses effectively.</p> Graphical Abstract <p>This graphical abstract provides a visual representation of the literature review conducted on Weather Index Insurance (WII) product design, pricing and hedging efficiency. Climate change is expected to increase the frequency of extreme weather conditions. WII is an innovative financial tool to protect farmers adverse weather events on crop yield. However the primary challenge of WII is basis risk when the weather index measured by weather stations or satellite data does not correspond to the actual crop yield loss, causing a misalignment in triggered WII payouts to the farmer. This study reviews 71 research papers, with a novel focus on studies which utilised quantitative and data-driven methodologies for WII parameter optimisation in product design and pricing models. The review identifies the need to explore remote sensing data and machine learning to create multi-variable weather indexes with improved accuracy. Further research is required to optimise contract parameters, incorporate phenological crop data to reduce basis risk, and develop sophisticated premium-setting models. Future research should focus on balancing model complexity with transparency and developing an integrated WII framework to optimise risk mitigation for the farmer. As the demand for WII grows, these advancements are critical for transforming WII into a reliable risk management tool, and we encourage readers to explore the full article for further insights.</p>