Evaluation of sub-daily precipitation variability and its diurnal characteristics in satellite and reanalysis datasets against ground-based observations

The diurnal cycle of precipitation serves as a fundamental diagnostic of the climate system's energy budget and hydrological cycle. Yet comprehensive evaluations of both its regular diurnal and irregular intermittent components across multiple satellite and reanalysis products against extensive ground-based observations remain limited. This study evaluates the boreal summer diurnal cycle across five satellite products and four reanalysis datasets against 6824 ground-based stations over the Northern Hemisphere land regions, complemented by inter-product comparison over quasi-global domain between 60° S and 60° N. Analyses are performed at 3-hourly resolution, combining sub-daily variance decomposition and harmonic analysis with atmospheric moisture budget diagnosis across contrasting precipitation regimes. Ground-based observations indicate sub-daily fluctuations account for 65.6 % of total precipitation variance, with the irregular component dominating over the repeatable diurnal signal, a fraction that all evaluated datasets systematically underestimate. Reanalysis datasets place the land precipitation maximum 3–6 h ahead of observations, driven by premature convective triggering. Among reanalysis datasets, the Japanese Reanalysis for Three Quarters of a Century (JRA-3Q) shows the smallest phase difference owing to its dynamic convective available potential energy (CAPE) generation rate (DCAPE)-based convective trigger, while the North American Regional Reanalysis (NARR) achieves superior performance over North America through direct assimilation of precipitation observations. Satellite products exhibit the observed late-afternoon precipitation maximum with a phase lag of 1–2 h, arising from the indirect detection of upper-tropospheric ice-scattering signals that persist as convective anvils expand beyond the timing of peak surface precipitation. Among satellite products, the Climate Prediction Center Morphing Technique (CMORPH) shows the closest agreement with observations, as its morphing-based propagation of passive microwave retrievals preserves precipitation timing while its probability density function (PDF)-based bias correction maintains sub-daily temporal structure. These findings provide practical guidance for dataset selection and offer actionable physical insights for improving convective parameterization schemes and satellite retrieval algorithms.