The value added by dynamically downscaling using a regional climate model, compared to using only a global climate model, is explicitly unveiled by using nontraditional skill evaluation statistics. The conventional model evaluation methods, such as temporal correlation of seasonal average rainfall, cannot always demonstrate the value of dynamically downscaled data. One of our primary alternative metrics for evaluating downscaling methodologies is comparing crop yields simulated using the downscaled data, assuming nonirrigated conditions, to yields simulated using observations. Rainfed crops in the southeast United States are very sensitive to water stress. In fact, these crops are often more sensitive to periods of wet/dry spells than to seasonal rainfall totals. Thus, using crop models as a performance metric provides an alternative to simply evaluating the prediction/simulation of the seasonal mean of some particular meteorological variables and has more practical relevance. Furthermore, while the discovery of climate signals contributing to total summertime precipitation variability remains elusive, our results suggest that dynamical regional models may better simulate intraseasonal variability than simply the anomalies of the seasonal mean, which further justify their usefulness in application models.
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