Evaluating the impact of model resolutions and cumulus parameterization on precipitation in NU-WRF: A case study in the Central Great Plains

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. •An object-based evaluation method is applied to analyze the spatial patterns in addition to traditional methods.•Limited improvement is found for precipitation forecast solely based on a finer horizontal model resolution in NU-WRF.•The model without the cumulus scheme shows more realistic precipitation at a 4-km resolution.•The impact of using cumulus scheme is much larger than the impact of model resolution.•A dry bias in precipitation days and a tendency of widespread precipitation with fewer times of precipitation were found.