Model parameter calibration is a fundamentally important stage that must be completed before applying a model to address practical problems. In this study, we describe an automatic calibration framework that combines sensitivity analysis (SA) and an adaptive surrogate modeling-based optimization (ASMO) algorithm. We use this framework to calibrate catchment-specific sensitive parameters for streamflow simulation in the variable infiltration capacity (VIC) model with a 0.25°spatial resolution over 10 major river basins of China from 1960 to 1979. We found that three parameters-the infiltration parameter (B) and two of the soil depth parameters (D 1 , D 2 )-are highly sensitive in most basins, while other parameter sensitivities are strongly related to the dynamic environment of the basin. Compared with directly calibrating the seven parameters recommended for the default calibration procedure, our framework not only reduced the computing time by two thirds through opting out of insensitive parameters (type I error) but also improved the Nash-Sutcliffe model efficiency coefficient (NSE) for optimized results when it identified a missing sensitive parameter (type II error) in the case study river basins. Results show that the SA-based ASMO framework is an effective and efficient model-optimization technique for matching simulated streamflow with observations across China. The NSE for monthly streamflow ranged from 0.75 to 0.97 and from 0.71 to 0.97 during the validation and calibration periods, respectively. The calibrated parameters can be applied directly in streamflow simulations across China, and the proposed calibration framework holds important implications for relevant simulation studies in other regions.

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Capsule summaryA long-term spatiotemporally continuous naturalized runoff record, CNRD v1.0, is reconstructed by using a comprehensive model parameter uncertainty analysis framework within a land-surface model.

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Documenting the spatiotemporal changes in vegetation cover and hydrological cycle of the Earth system and understanding how they interact are important especially under climate warming. In this research, we quantified the changes in vegetation and evapotranspiration (E) across China during 1982–2015 and then revealed the complex relationships in climate–vegetation–evapotranspiration system. Results show that the upward trend in vegetation leaf area index (LAI) during 2000–2015 (an increase of 0.95% per year) was almost 8 times the trend during 1982–1999. The zones of the Loess Plateau and the Three‐North Shelter Forest Program are the most notable areas for LAI increases between these two periods, with increases of 11.65% and 2.87%, respectively. Increased LAI, along with the warming climate, has accelerated E across China in the past several decades, and the annual increase in the E rate was 0.34% (1.34 mm year−1) during 1982–1999 and 0.40% (1.62 mm year−1) during 2000–2015. The zones of the Loess Plateau and the karst landform are the most notable areas for transpiration increases, with individual increases of 10% and 5%, respectively. In general, the dominant causes for evaporation changes across all of China are temperature and precipitation, while the main reasons for transpiration changes include temperature, LAI, and sunshine duration. This study improves our understanding of the relationships within the climate–vegetation–evapotranspiration system and provides important support for future ecological policies across China.

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Decomposing the uncertainty of global climate models is highly instructive in understanding climate change. However, it remains unclear whether sources of uncertainty have changed as the models have evolved and the extents to which uncertainty in temperature and precipitation are narrowed after bias correction (BC). We quantified uncertainty in temperature and precipitation projections over global land from three sources—model uncertainty, scenario uncertainty, and internal variability—and compared results from the models participating in the 5th and 6th phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). In addition, we investigated the potential of four BC methods for narrowing uncertainty in temperature and precipitation over the globe and individual continents. Raw projections of temperature and precipitation have greater uncertainty and lower fractional uncertainty relative to their anomalies. The largest temperature uncertainties appear in high‐latitude and high‐altitude regions, and the largest precipitation uncertainties are in low‐latitude regions and mountainous and coastal areas. For uncertainties in CMIP6 temperatures, the contribution from model uncertainty decreases with time (from 99% to 39%), while the contribution from scenario uncertainty increases with time (from 0.01% to 61%). For precipitation projections, the contribution from model uncertainty predominates (98%), while the contributions from scenario uncertainty (1.8%) and internal variability (0.2%) are extremely low. Four BC methods have exhibited excellent ability to reduce uncertainty, and among them, BC and spatial disaggregation has the best performance. These findings can help us better understand the characteristics of the models, while also providing decision makers with more accurate information to address climate mitigation and adaptation measures.