Improving risk reduction potential of weather index insurance by spatially downscaling gridded climate data - a machine learning approach

Open-access gridded climate products have been suggested as a potential source of data for index insurance design and operation in data-limited regions. However, index insurance requires climate data with long historical records, global geographical coverage and fine spatial resolution at the same time, which is nearly impossible to satisfy, especially with open-access data. In this paper, we spatially downscaled gridded climate data (precipitation, temperature, and soil moisture) in coarse spatial resolution with globally available long-term historical records to finer spatial resolution, using satellite-based data and machine learning algorithms. We then investigated the effect of index insurance contracts based on downscaled climate data for hedging spring wheat yield. This study employed county-level spring wheat yield data between 1982 and 2018 from 56 counties overall in Kazakhstan and Mongolia. The results showed that in the majority of cases (70%), hedging effectiveness of index insurances increases when climate data is spatially downscaled with a machine learning approach. These improvements are statistically significant (p≤0.05). Among other climate data, more improvements in hedging effectiveness were observed when the insurance design was based on downscaled temperature and precipitation data. Overall, this study highlights the reasonability and benefits of downscaling climate data for insurance design and operation.