This study aims to evaluate the performance of five global medium-range operational NWP model rainfall forecasts, namely NCUM, UKMO, IMD GFS, NCEP GFS and ECMWF to provide an intercomparison of rainfall forecasts over India in terms of skill in predicting daily rainfall (24-hr accumulated rainfall). Veri- fication and intercomparison of rainfall forecasts over India during monsoon 2020 (JJAS) are carried out using both (i) standard traditional verification methods (POD, FAR, RMSE, etc.) and (ii) advanced spatial verification methods (MODE, FSS). The evaluation also includes assessment of large-scale mean patterns, temporal evolution of spells during the season, dominant modes using spectral analysis, basin-scale rainfall time series and isolated heavy rainfall cases. Our analysis suggests that some of the key large-scale aspects of monsoon (seasonal mean, active/break spells, and northward propagation) are realistically represented in all the models, with slight discrepancies. In addition, the spectral analysis of rainfall is in association with observed rainfall in Day-1 forecast and deteriorates with lead times. Synoptic variance in NCUM on longer leading times is closer to observations. While the standard categorical verification over India as a whole (spatial averaged) suggests that ECMWF forecast skill is relatively high among the Bve models, the veri-fication over the sub-regions shows mixed results with no clear unique higher performer among the models. In addition, basin-scale verification of rainfall forecasts for five rivers over the Indian subcontinent shows a fairly good amount of skill in terms of CC and RMSE up to Day-3 with comparable scores among the models. The advanced spatial verification metrics, like MODE and FSS, applied to the models show varying skills with different attributes. However, for FSS, forecast skill was high (low) for lower (higher) rainfall thresholds of 20 mm/day (100 mm/day). Though different models with different spatial resolutions show reasonable skill scores for larger regions, for high-impact heavy rainfall events, which are generally localised, the models have very comparable poor skill with no clear edge by a model among the five models.