Abstract: In response to the disruption of observation collection or transformation by extreme disastrous weather and subsequent destruction of automatic weather stations and communication facility, upstream and downstream relationships of grid real-time analysis products are investigated for data interpolation, and the feasibility of carrying out disaster warning and emergency decision-making services are analyzed, from the perspective of Meteorological Big Data Cloud Platform (Tianqing). Through calling the location based services interface built on high-resolution multi-source fusion grid real-time analysis products, 3 methods of data interpolation services are proposed, including interpolation service based on national-provincial collaborative method, MDOS (Meteorological Data Operational System) correction, and special topics database. Advantages and disadvantages of 3 methods are compared in terms of data consistency, service timeliness, operational complexity, and automation level, among other factors. The optimal interpolation scheme suitable for different application scenarios is provided, such as joint disaster emergency response by the national and provincial meteorological department, or independent rapid emergency services by provincial meteorological department, as well as the situation where there is only a small number of missing observation stations in the early stages of disasters. The overall application and deployment of these methods in meteorological departments across the country is introduced, combined with the effect verification of 3 typical disaster-causing heavy precipitation weather process cases, including the heavy rainfall at Shangluo of Shaanxi on 19 July 2024, in northern Shaanxi region on 9 August 2024, and the heavy rainfall caused by super stronger Typhoon Capricorn (2411) on 6 September 2024. Results indicate that the multi-source fusion grid real-time interpolation station service method can effectively and timely fill in missing observation values, which could make contribution to improve the ability to monitor weather precisely, and has important data support significance for analyzing, warning, and emergency guarantee services of disastrous weather.