Regional climate models are expected to exhibit improved skill at finer spatial resolutions due to improved representation of land surface heterogeneity. However, at spatial scales between 1 and 10 km (grey scales), these improvements are often illusive due to the competing benefits from spatial resolution and cumulus parameterization. This study provides insights into the impact of model resolution and cumulus parameterization on precipitation prediction in the Central Great Plains by using an object-based evaluation method. Our results show limited improvement solely from finer resolution but larger improvement without using the cumulus scheme at a 4-km resolution. Compared to traditional evaluation methods, the object-based analysis shows that without the cumulus scheme the spatial properties of precipitation are better represented. In contrast, all model configurations show a dry bias in precipitation days and a tendency to produce widespread precipitation but with fewer hours with precipitation which indicates other shortcomings in the model.

由于地表异质性的表示得到改善，预计区域气候模型将在更精细的空间分辨率下表现出更高的技能。然而，在 1 到 10 公里（灰度）的空间尺度上，由于空间分辨率和积云参数化的竞争优势，这些改进通常是虚幻的。本研究使用基于对象的评估方法，深入了解模型分辨率和积云参数化对大平原中部降水预测的影响。我们的结果表明，仅通过更精细的分辨率获得的改进有限，但在 4 公里分辨率下不使用积云方案的改进更大。与传统的评价方法相比，基于对象的分析表明，在没有积云方案的情况下，降水的空间特性可以更好地表现出来。相比之下，