Abstract: To evaluate the applicability of national-level multi-source merged precipitation products (ART_5 km and ART_1 km, hereinafter referred to as the two products) over the Hanjiang waterway, this study employs binary (categorical) and continuous-variable metrics to systematically assess their performance over the Hanjiang waterway during 2022–2023, in terms of precipitation detection, intensity estimation, spatiotemporal distribution differences, and typical precipitation events. The results show that: (1) The two products exhibit limited skill in distinguishing the occurrence of precipitation; however, for samples where precipitation has actually occurred, both show good correlations with meteorological station observations, with only slight systematic underestimation (hourly scale: −0.2 mm·h^-1; daily scale: −0.3 mm·d^-1), indicating good overall stability. (2) The performance of both products depends markedly on precipitation intensity. They perform best in detecting and estimating weak precipitation (<2 mm·h^-1 or light rain); at the hourly scale, heavy precipitation is subject to pronounced systematic underestimation, and extreme precipitation is prone to missed detection, whereas at the daily scale, precipitation of moderate rain and above still shows good correlations (R > 0.9). (3) Spatial variability is significant. The upper reaches show the largest quantitative estimation errors; the middle reaches yield the best binary detection performance; the lower reaches show degraded detection skill and concentrated errors under heavy precipitation, though the ability to capture process evolution remains satisfactory. (4) Temporal differences are evident. Weak precipitation has the smallest and most stable errors throughout the year; moderate-intensity precipitation (2~20 mm·h^-1, or moderate to heavy rain) is best detected during March–April and September–October, with relatively small quantitative biases; heavy precipitation is better detected during June–August, though accompanied by large spatial dispersion, large afternoon errors, and partial missed detection of extreme precipitation. (5) Analysis of typical precipitation events shows that both products can effectively capture the spatiotemporal evolution of precipitation over the Hanjiang waterway (rainband movement, location and structure of heavy precipitation centers); ART_5 km performs better in monitoring precipitation below 20 mm·h^-1, while ART_1 km has advantages in heavy precipitation detection. These results demonstrate that the ART_5 km and ART_1 km products can provide data support for shipping warnings at different risk levels along the Hanjiang waterway, and that heavy precipitation estimates, after threshold optimization, remain of reference value.