In the context of water scarcity, the strong spatiotemporal fluctuation of rainfall combined with the sparsity of the rain gauge (RG) measurement networks, particularly over the mountainous regions, constitute real constraints for water resources management. In recent decades, several satellite-based rainfall estimates have become available. These estimates can be a potential source of data over poorly gauged watersheds. Thus, the main aim of this work was to investigate the reliability of eight spatial rainfall product (SRP) datasets (ARC-2, CHIRPSp25, CHIRPSp5, CMORPH-CRT, GPM-IMERG, PERSIANN-CDR, RFE-2, and TRMM-3B42) in reproducing the observed daily and monthly rainfall in semiarid watersheds. The SRP estimates were evaluated by direct comparison against the RG observations. Then, bias correction techniques were used to account for the inherent bias in the SRPs. The results indicated that the rainfall products were poorly correlated with the daily observed rainfall patterns, with Pearson correlation coefficients (PCCs) mostly below 0.5, but agreed well with the monthly observations. The agreement was more robust over the lowlands than the mountainous region, with IMERG (with a short-term record) and PERSIANN (with a long-term record) providing the best performance. Nevertheless, the monthly SRP estimates were significantly biased, as the large rainfall totals were frequently underestimated. However, when the bias correction was applied, remarkable performance improvement was observed in the SRP. Linear scaling (LS) and simple linear regression (SLR) were better in reducing the bias than random forest (RF) and cumulative distribution function (CDF), as they were more consistent when validated on independent data. Overall, the efficiency of the correction techniques was found to be sensitive to the difference in climatology between the reference and validation contexts.