Index insurance requires historical weather and yield data to establish statistical relationships between selected indices (e.g., rainfall, temperature) and agricultural losses (Kath et al., 2018; Bucheli et al., 2021; Tan and Zhang, 2024; Kanchai et al., 2024). These relationships inform critical contract parameters, including payout triggers, compensation levels, and premium rates (Kath et al., 2018; Kanchai et al., 2024). For example, studies on sugarcane in Australia employed 80 years of historical climate and yield data to calibrate an excessive rainfall index (Kath et al., 2018), while drought insurance for wheat relied on 31 years of analogous data (Kath et al., 2019). Underpinning this approach is the assumption that past weather-yield dynamics and extreme event distributions remain stable over time (Tan and Zhang, 2024; Williams and Travis, 2019). Yet, this assumption is becoming untenable in the face of climate change. Rapidly shifting climatic patterns are not only elevating the frequency of extreme weather events but also amplifying their severity (Kath et al., 2018; Pan et al., 2022; Benso et al., 2023; Eltazarov et al., 2023). Consequently, historical datasets may fail to accurately capture future risks (Tan and Zhang, 2024; Williams and Travis, 2019; Osgood et al., 2024). This discrepancy can lead to systemic underestimation of liabilities, resulting in mispriced insurance products that jeopardize insurer solvency (Tack and Ubilava, 2015; Osgood et al., 2024). Alternatively, insurers may be compelled to raise risk premiums to compensate for the uncertainty of future losses, potentially reducing affordability and accessibility for farmers.

Applications of index insurance in the real world have varying outcomes. For instance, effective schemes in India and Kenya have realized increased farmer resilience with the utilization of satellite-based drought indices (Murthy et al., 2024; Singh and Agrawal, 2019). Conversely, other failed applications in West Africa and Southeast Asia reveal some of the challenges, such as index calibration challenges and farmer mistrust that led to low adoption and user dissatisfaction (Bucheli et al., 2022; Osgood et al., 2024). These cases indicate the importance of context-specific design and participatory approaches.

The accelerating effects of climate change require major reforms in the design and implementation of index insurance schemes. A growing body of research shows that traditional single-variable indices such as seasonal rainfall totals are increasingly insufficient to capture complex non-linear relationships between climate variables and agricultural outcomes (Singh and Agrawal, 2019; Tsiboe et al., 2023). This constraint has prompted the development of more sophisticated indices that better reflect biophysical reality, including growing degree days calibrated to crop phenology (Conradt et al., 2015), composite indices incorporating multiple climatic variables (Murthy et al., 2024), and indices for specific climatic events such as heat waves and precipitation (Benso et al., 2023).

Recent advances in data availability and analytical techniques are changing the concept of index insurance design. Satellite-derived remote sensing data now allow for near-real-time monitoring of soil moisture, vegetation health, and microclimate conditions at unprecedented spatial resolution (Abdi et al., 2022; Osgood et al., 2024). Combined with gridded climate model output (Pan et al., 2022; Eltazarov et al., 2023), these data sets allow for a more accurate assessment of the risks while addressing the critical problem of baselines in regions where data is scarce. Analytic innovation is also transforming, with AI and machine learning algorithms showing particular promise in capturing complex non-linear weather patterns that are often overlooked by traditional statistical methods (Chen et al., 2024; Tan and Zhang, 2024). Further methodological advances, including crop models (Will et al., 2021), quantile regression for extreme event analysis (Kath et al., 2018; Abdi et al., 2022) and dynamic factor models for multivariate climate integration (Li et al., 2022), allow for more robust modeling of extreme events.

As climate change progresses, agricultural systems increasingly face compound risks characterized by simultaneous or sequential hazards, which is a major constraint for index insurance products that use a single index (Benso et al., 2023). The use of single indices leaves farmers exposed to other climate threats. Emerging solutions include the development of composite indices integrating multiple stress factors (e.g., heat and humidity stress) and innovative triggers that account for the sequence of hazards (Kanchai et al., 2024; Kath et al., 2018). However, these technological innovations need to be accompanied by equally important actuarial reforms.

Recent evidence in India has shown that farmers have suffered crop losses due to climate-related factors such as droughts, floods, hailstorms, and pest infestations (Reddy, 2025). Critically, climate change does not only increase extreme weather events, but also changes the underlying relationships between climate variables and agricultural performance. Changing seasons, new damage thresholds and changing patterns of water availability (Tan and Zhang, 2024; Wang et al., 2021; Bucheli et al., 2022) are weakening the predictive power of the historical models. For example, rising temperatures in the US are already projected to cause higher crop insurance premiums (Tack et al., 2018). The reliance on outdated data exacerbates the basis risk, which undermines the value of the instrument as a risk management tool (Tappi and Santeramo, 2022; Tsiboe et al., 2023; Chen et al., 2024). Recent years, which better reflect current climatic conditions and technological developments, may provide more relevant insights than remote historical records (Tan and Zhang, 2024). Given these concerns, it is prudent for actuarial models to integrate forward-looking methods.

The non-stationary nature of modern climate systems makes historical data insufficient for insurance purposes (Tappi and Santeramo, 2022). Forward-looking approaches must explicitly include climate projections through three key mechanisms: (1) statistical adjustment of historical data using for example the Intergovernmental Panel on Climate Change (IPCC) climate scenarios, (2) simulation of yield responses under future climatic conditions, and (3) systematic testing of insurance portfolios against high-impact climate scenarios (Osgood et al., 2024; Benso et al., 2023; Zhang et al., 2022; Tan and Zhang, 2024). This paradigm shift from reactive to anticipatory risk modeling strengthens index insurance's resilience to climate shocks. However, realizing this resilience in practice requires policy interventions.