Extreme weather events, such as heatwaves, droughts, and excess rainfall, are a major cause of crop yield losses and food insecurity worldwide. Statistical or process-based crop models can be used to quantify how yields will respond to extreme weather and future climate change. However, the accuracy of weather-yield relationships derived from crop models, whether statistical or process-based, is dependent on the quality of the underlying input data used to run these models. In this context, a major challenge in many developing countries is the lack of accessible and reliable meteorological datasets. Gridded weather datasets, derived from combinations of in-situ gauges, remote sensing, and climate models, provide a solution to fill this gap, and have been widely used to evaluate climate impacts on agriculture in data-scarce regions worldwide.