Flooding is an endemic global challenge with annual damages totalling billions of dollars. Impacts are felt most acutely in in low and middle-income countries, where rapid demographic change is driving increased exposure. These areas also tend to lack high precision hazard mapping data with which to better understand or manage risk. To address this information gap a number of Global Flood Models have been developed in recent years. However, there is substantial uncertainty over the performance of these data products.

This study examines the impact of climate variability on household welfare outcomes in Uganda by combining long-term climate data (1979-2013) interpolated at household level, and six waves of the Uganda National Panel Survey (2009-2019). Pooled average ordinary least squares and random effects models are used for empirical analysis. The results indicate that climate variability has a significant nonlinear impact on household welfare outcomes.