 This paper evaluates eight precipitation estimates derived from four different satellite retrieval algorithms, including TRMM multisatellite precipitation analysis, TMPA, climate prediction center morphing technique, CMRPH, global satellite mapping of precipitation, GSMA-P, and precipitation estimation from remotely sensed information using artificial neural networks, Persian. The original satellite and bias-corrected products of each algorithm are evaluated against ground-based Asian precipitation highly resolved observational data integration towards evaluation of water resources, Aphrodite, over Central Asia for the period of 2004 to 2006. The analyses show that all products except Persian exhibit overestimation over Aral-C and its surrounding areas. Bias-correction improves the quality of the original satellite TMPA products and GSMA-P significantly but slightly in CMRPH and Persian over Central Asia. 3B42RTV7 overestimates precipitation significantly with large relative bias, RB, while GSMA-P underscore gauge shows consistent high correlation coefficient, CC, but RB fluctuates between minus 57.95% and 112.63%. The Persian underscore CDR outperforms other products in winter with the highest CC. Both the satellite-only and gauge-adjusted products have particularly poor performance in detecting rainfall events in terms of lower pod, less than 65%, CSI, less than 45%, and relatively high-far, more than 35%. This article was authored by Haogua, Xingqin, Anmingbao, and others. We are article.tv, links in the description below.