Evaluating the utility of weather generators in crop simulation models for in-season yield forecasting

Crop yield forecasting is crucial for ensuring food security and adapting to the impacts of climate change, as it provides early insights into potential harvest outcomes and helps farmers and policymakers make informed decisions in the face of changing environmental conditions. The accuracy of the crop model–based yield forecasting frameworks is affected by the uncertainty in future weather data, which is often substituted with synthetic weather realizations generated by stochastic weather generators. This study aims to assess the performance of three recent stochastic weather generators—Global Weather Generator (GWGEN), WeatherGEN, and R Multi-Sites Autoregressive Weather GENerator (RMAWGEN) — in producing synthetic weather realizations that accurately represent regional climate variations and their impact on winter wheat yield forecasting. We utilized historical weather data from Daymet, an interpolation of daily meteorological observations that produces gridded datasets with a spatial resolution of 1 km. This data was used both as an input for the weather generators and for evaluating the performance of the generated weather realizations. Furthermore, the weather realizations generated by these weather generators across multiple winter wheat field sites in Kansas were employed in the calibrated Environmental Policy Integrated Climate (EPIC) crop model to assess the potential impact of variations in weather generators on the accuracy of crop yield forecasts. RMAWGEN and WeatherGEN excelled in accurately simulating rainy days and precipitation amounts, with WeatherGEN particularly effective in wet months and RMAWGEN performing best in dry months, showcased their proficiency in diverse weather conditions. RMAWGEN consistently showed lowest error across all variables, including precipitation, solar radiation, and both maximum and minimum temperatures. Except for GWGEN, both RMAWGEN and WeatherGEN demonstrate good agreement with Daymet in replicating spatial variability patterns. RMAWGEN notably outperformed other weather generators, particularly during the forecasting period. Consequently, it showed superior capabilities in forecasting crop yields closely matching the simulated results with Daymet data. The findings of this study are crucial for selecting accurate weather data estimates for crop yield forecasting. Utilizing alternative sources such as ensembles of multiple weather generators or outputs from sub-seasonal multi-model forecast systems may furt