[Objective] This study aims to reveal the seasonal concentration characteristics and spatial differentiation patterns of precipitation in Tianjin from 1980 to 2023 by quantifying long-term seasonal trends， characterizing the spatial patterns of Precipitation Concentration Degree （PCD） and Precipitation Concentration Period （PCP）， and establishing a bivariate risk assessment method based on PCD-PCP joint return periods using Copula functions. The innovation lies in the first application of Copula-based PCD-PCP coupled analysis to the hydrometeorological field of Tianjin， providing support for extreme flood prevention. [Methods] Daily precipitation data from 13 meteorological stations in Tianjin during 1980-2023 were employed. The Mann-Kendall trend test and Sen’s slope estimator were used to detect monotonic trends in seasonal and annual precipitation. The vector-based PCD and PCP indices were calculated to quantify precipitation concentration uniformity and peak timing. Inverse distance weighting （IDW） was applied to visualize spatial patterns. The distribution characteristics of PCD-PCP under specific scenarios were analyzed based on copula joint distribution. [Results] Seasonal precipitation in Tianjin is highly uneven. Summer dominates with a mean of 383.6 mm （70.1% of annual total）， while winter precipitation is extremely low （11.5 mm） but shows the highest interannual variability （extreme value ratio： 180.50）. Summer precipitation exhibits a significant increasing trend， which may elevate the risk of extreme precipitation events， whereas the increased variability of autumn precipitation could prolong the urban waterlogging risk window. Spatially， annual precipitation exhibits a stable “higher in the north and lower in the south” pattern across the 1980s-2010s. The spatial pattern of PCD shows limited variability， with values predominantly ranging from 0.68 to 0.73. A high-value zone is identified in the Binhai New Area （PCD≈0.73）， reflecting strong precipitation concentration within a short annual window， whereas lower values in Jizhou and Wuqing suggest a more even precipitation regime. In contrast， PCP exhibits a west-east gradient， increasing from approximately 201 in the west to 207 in the east （mid-to-late July）， indicating spatial asynchrony in precipitation concentration timing. Temporally， PCD shows substantial interannual fluctuations （0.50-0.85）， yet its long-term trend remains stable， as evidenced by decadal means ranging from 0.69 to 0.71. PCP also exhibits notable interannual variability （180-220） but demonstrates a significant decadal delay， progressing from 198.5 in the 1980s to 203.7 in the 2010s， a trend intrinsically linked to the phenomenon of summer rainfall shifting to autumn in the Beijing-Tianjin-Hebei region. The copula-based analysis reveals a non-independent relationship between PCD and PCP， whereby higher PCD values tend to coincide with PCP falling within the annual peak precipitation period. The 50-year precipitation event corresponds to the combination of high PCD and a specific PCP， indicating that this extreme scenario arises from the simultaneous deviation of both variables from their normal states. Under this scenario， PCD is significantly above the multi-year average， and PCP falls within the main summer flood season. This concurrence of high precipitation concentration and flood season timing will substantially elevate the risk of urban waterlogging and basin flooding. Therefore， responding to extreme precipitation events requires attention not only to increases in total precipitation but also to high temporal concentration and its coincidence with the main flood season. [Conclusion] This study systematically quantifies the seasonal concentration characteristics and spatial differentiation of precipitation across Tianjin. Summer dominates both the total amount and long-term trends. PCD is spatially homogeneous but interannually variable， whereas PCP shows a clear west-east gradient. Under the 50-year precipitation event， Tianjin faces credible extreme flood hazards. Future work should incorporate climate model projections to assess non-stationarity in the PCD-PCP dependence structure under warming scenarios.