This study deals with the integration of ingmerging of highly accurate precipitation estimates from Global Precipitation Measurement (GPM) with sampling gap-free satellite observations from Meteosat 7 of Meteosat First Generation (MFG) to develop a regional rainfall monitoring algorithm for monitoring precipitation over India and nearby oceanic regions. For this purpose, we derived precipitation signatures from Meteosat observations to co-locate it against precipitation from GPM. A relationship is then established between rainfall and rainfall signature using observations from various rainy seasons. The relationship thus derived can be used to monitor precipitation over India and nearby oceanic regions. Performance of this technique was tested against rain gauges and global precipitation products including the Global Satellite Mapping of Precipitation (GSMaP), Climate Prediction Centre MORPHing (CMORPH), Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIANN) and Integrated Multi-satellitE Retrievals for GPM (IMERG). A case study is presented here to examine the performance of the developed algorithm for monitoring heavy rainfall during flood event of Tamil Nadu in 2015. The present algorithm shows a bias of -2.72, a Root Mean Square Error of 10.82, a Correlation Coefficient of 0.76 and a skill score of 0.36 when compared with IMD gridded rainfall product at 0.25⁰