<p>Agricultural producers face increasing challenges with climate change, as floods and droughts affect crop yields. Index insurance based on weather derivatives has grown rapidly in the past decades, but challenges remain. The existing models are designed to provide protection against either flooding or droughts, but rarely for both. Additionally, remote areas in emerging economies lack data that are needed for a more precise and thus fair-priced insurance index. We propose a two-step approach to manage flooding and drought risks through weather index insurance. In the first step, a weather index is constructed and forecasted. In the second step, we use a bivariate copula model with dynamic quantiles. We apply this approach to optimize the hedging strategy for coffee producers in the province of Samana in Colombia for the period 2007–2019, and forecasts for 2020–2028. We find that the optimal index insurance model is based on a copula structure with dynamic quantiles. This model can reduce basis risk, as measured by the improvement of hedging effectiveness, compared to benchmark models: We find hedging effectiveness of 60% while other studies find up to 43%. Compared to current programs for coffee crops, the insurance premiums based on our approach are up to five times cheaper. Our results can be useful for policy makers and agricultural producers, as higher hedging effectiveness and more affordable index insurance contracts allow more producers to use index insurance. Our approach can be replicated in other low-income regions where data is challenging to obtain from weather stations.</p>