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

Abstract. 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 6,824 ground-based stations over the Northern Hemisphere land regions, complemented by inter-product comparison over global ocean and Southern Hemisphere domains where dense sub-daily gauge networks are unavailable. This study combines 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 66.3 % of total precipitation variance, with the irregular component dominating over the repeatable diurnal signal, a fraction that all evaluated datasets systematically underestimate. Satellite products reproduce the observed late-afternoon precipitation maximum with a systematic phase lag of 1–2 hours, attributable to passive microwave detection of upper-tropospheric ice-scattering signals rather than surface precipitation, while sub-daily variance fidelity differs substantially among products depending on calibration methodology. Reanalysis datasets exhibit a more severe premature phase lead of 3–6 hours over land, driven exclusively by premature convective triggering. Among reanalysis datasets, JRA-3Q shows the smallest phase difference owing to its DCAPE-based convective trigger, while NARR achieves superior performance over North America through direct assimilation of precipitation observations. Among satellite products, CMORPH shows the closest agreement with observations, as its morphing-based propagation of passive microwave retrievals preserves precipitation timing while its 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.