Satellite Precipitation Products (SPP) have been revolutionary in water resources management and flood-related disaster response. However, estimating extreme rainfall is subject to multiple systematic and aleatory errors that need to be corrected. This dissertation addresses errors in satellite data to estimate extreme rainfall events in space and time beyond the pixel. The Spatiotemporal Contiguous Object-based Rainfall Analysis method (ST-CORA) is developed to analyse errors in SPP for rainstorm estimations based on their main physical features in space and time (volume, intensity, duration, extension, orientation, speed, among others). Using ST-CORA, systematic errors due to volume and displacement in space and time are corrected in a novel bias-corrected method called ST-CORAbico. Case studies in two monsoonal areas in South America and Southeast Asia have been used to analyse the hydrological response of systematic errors in flood predictions and evaluate error reduction in non-operational and operational bias correction applications. Finally, the dissertation describes further implementations of ST-CORA in developing an operational system for rainstorm monitoring called Rainstorm tracker. This web-based platform is designed to monitor and alert decision-makers about the severity of rainstorm events over the Lower Mekong basin in near-real and real-time.