Rain gauge networks are not adequate to capture the complex spatial structure of rainfall for meaningful urban flood studies. While radar and satellite precipitation products (SPPs) are being developed to alleviate the limitations of rain gauge networks, they need to be evaluated for regional applications. This study compared radar against 3 SPPs of IMERG (Integrated Multi-satellite Retrievals for Global Precipitation Measurement), MSWEP (Multi-Source Weighted-Ensemble Precipitation) and PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System) that have sub-daily records for southeast Queensland, Australia, based on the spatial structure identified through the structural similarity index (SSI) and two-dimensional correlogram at the daily timescale. The SPPs were downscaled to 1 km × 1 km grid resolution to conform with the radar dataset using inverse distance weighting and bilinear interpolation. All three SPPs, that are freely available, reflected very well the similarity (>90 %) in the spatial pattern with that of the radar, although IMERG was marginally better. However, MSWEP appeared to be the closest to the gauge dataset, better than even the expensive radar dataset, stemming from its much better similarity in the mean similarity statistic. Nevertheless, there could be some atmospheric climatic factors that may cause one to select one SPP over the others for a given wet day, and this needs to be explored for the possibility of selecting different SPPs for different wet days.