Calibrated coefficients for high-resolution downscaling: A 1-km gridded daily dataset of temperature and precipitation across the Contiguous United States from NMME Seasonal forecasts

The coarse resolution of the North American Multi-Model Ensemble (NMME) often introduces biases and uncertainties when applied to regional and local scales, limiting its applications in crop modeling and irrigation management. To address these limitations, we employed a statistical downscaling method with bias correction for both mean and variability. This approach was applied to the Canadian Coupled Climate Model version 4 (CanCM4), a representative model within the NMME, to generate 1-km gridded daily weather projections for maximum and minimum air temperatures and precipitation across the contiguous United States (CONUS). The downscaled hindcast projections were calibrated using the Daily Surface Weather and Climatological Summaries (DAYMET) dataset. This dataset provides the calibrated coefficients necessary to produce 1-km gridded daily weather projections, offering a valuable resource for applications such as regional crop modeling and irrigation management. Details can be found in our paper: Su, Q., Ale, S., Himanshu, S., Singh, J., and Singh, V.P. (2025). Calibration and bias correction of seasonal weather forecasts from the North American Multi-Model Ensemble: Potential applications for regional crop modeling and irrigation management. Journal of Agricultural Science 1-14. https://doi.org/10.1017/S0021859625000139