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Land surface models such as the Community Land Model version 5 (CLM5) seek to enhance understanding of terrestrial hydrology and aid in the evaluation of anthropogenic and climate change impacts. However, the effects of parametric uncertainty on CLM5 hydrologic predictions across regions, timescales, and flow regimes have yet to be explored in detail. The common use of the default hydrologic model parameters in CLM5 risks generating streamflow predictions that may lead to incorrect inferences for important dynamics and/or extremes. In this study, we benchmark CLM5 streamflow predictions relative to the commonly employed default hydrologic parameters for 464 headwater basins over the conterminous United States (CONUS). We evaluate baseline CLM5 default parameter performance relative to a large (1,307) Latin Hypercube Sampling‐based diagnostic comparison of streamflow prediction skill using over 20 error measures. We provide a global sensitivity analysis that clarifies the significant spatial variations in parametric controls for CLM5 streamflow predictions across regions, temporal scales, and error metrics of interest. The baseline CLM5 shows relatively moderate to poor streamflow prediction skill in several CONUS regions, especially the arid Southwest and Central U.S. Hydrologic parameter uncertainty strongly affects CLM5 streamflow predictions, but its impacts vary in complex ways across U.S. regions, timescales, and flow regimes. Overall, CLM5's surface runoff and soil water parameters have the largest effects on simulated high flows, while canopy water and evaporation parameters have the most significant effects on the water balance. Plain Language Summary: Large‐scale land surface computer models, such as the Community Land Model version 5 (CLM5), play a central role in projecting future changes in water resources and hydrological extreme events such as floods and droughts. These models require representations of land surface processes and properties that vary significantly across space. Consequently, these representations require a large suite of input parameters to distinguish different soils, vegetation, land‐cover, and other features of the landscape that shape flood and drought dynamics. Given the large number of these input parameters required by CLM5 and limits in the information needed to specify them, we hypothesize that they strongly increase uncertainty in our ability to predict floods or droughts. In this study, we develop and apply a diagnostic approach to better understand how CLM5 input parameter uncertainty influences water predictions and the relative importance of different input parameters across the continental United States. We argue and illustrate that addressing model input parameter uncertainty is crucial as the results of uncertainty characterization may have important implications for decision making. This study provides region‐specific insights into CLM5 model input parameter uncertainty and dominant processes for a variety of hydrologic application contexts. Key Points: Community Land Model Version 5 (CLM5) streamflow predictions use default parameters are vulnerable to poor performance especially in the arid Southwest and Central U.SThe effects of CLM5 hydrologic parameter uncertainty are significantly higher for drought versus flood predictions in humid regionsThe dominant hydrologic parameters in CLM5 vary significantly across regions, diagnostic error metrics, and temporal scales [ABSTRACT FROM AUTHOR]

Land surface models such as the Community Land Model Version 5 (CLM5) are essential tools for simulating the behavior of the terrestrial system. Despite the extensive application of CLM5, limited attention has been paid to the underlying uncertainties associated with its hydrological parameters and how these uncertainties affect water resource applications. To address this long-standing issue, we use five meteorological datasets to conduct a comprehensive hydrological parameter uncertainty characterization of CLM5 over the hydroclimatic gradients of the conterminous United States. Key datasets produced from the uncertainty characterization experiment include: a benchmark dataset of CLM5 default hydrological performance, parameter sensitivities for 28 hydrological metrics, and large-ensemble outputs for CLM5 hydrological predictions. The presented datasets will assist CLM5 calibration and support broad applications, such as evaluating drought and flood vulnerabilities. The datasets can be used to identify the hydroclimatological conditions under which parametric uncertainties demonstrate substantial effects on hydrological predictions and clarify where further investigations are needed to understand how hydrological prediction uncertainties interact with other Earth system processes. [ABSTRACT FROM AUTHOR]