Abstract: A new grouping Z-R relationships fitting scheme is proposed based on the storm information derived by the storm-identification algorithm to reduce the error of quantitative precipitation estimation (QPE), by which different Z-R relationships are calculated and utilized respectively within separated storms identified by storm identification algorithm or data groups within the same storm to retrieve the surface rainfall information. Case study is presented by using the Meiyu front event between 05 and 07 June 2013, which covered the whole Middle-Lower Yangtze Plain. Using 28 S-band Doppler radar and 11623 minute-by-minute gauge observations, the unique dynamical Z-R relationship, simple grouping Z-R relationships and Z-R grouping relationships based on storm identifications are compared using radar 1-hour quantitative precipitation estimation derived respectively, the results show that (1) the storm-grouping relationships fitting scheme can effectively capture the local data characteristics which make it superior to the other two fitting scheme.(2) the weaker radar echoes introduced by radar beam shielding not only accounts for the discontinuity of radar QPE but affects the uncertainty of the Z-R relationships.(3) the stronger or weaker radar echoes resulted by radar hardware or the calibration associates with the overestimation or underestimation of radar QPE derived by the simple grouping Z-R relationships and make it unsuitable for the application in the large-scale rainfall event, but the impact on the storm-grouping relationships fitting scheme is relatively smaller.