  Over the past year, data-driven global weather forecasting has emerged as a
new alternative to traditional numerical weather prediction. This innovative
approach yields forecasts of comparable accuracy at a tiny fraction of
computational costs. Regrettably, as far as I know, existing models exclusively
rely on regression losses, producing forecasts with substantial blurring. Such
blurring, although compromises practicality, enjoys an unfair advantage on
evaluation metrics. In this paper, I present Kunyu, a global data-driven
weather forecasting model which delivers accurate predictions across a
comprehensive array of atmospheric variables at 0.35{\deg} resolution. With
both regression and adversarial losses integrated in its training framework,
Kunyu generates forecasts with enhanced clarity and realism. Its performance
outpaces even ECMWF HRES in some aspects such as the estimation of anomaly
extremes, while remaining competitive with ECMWF HRES on evaluation metrics
such as RMSE and ACC. Kunyu is an important step forward in closing the utility
gap between numerical and data-driven weather prediction.
