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The Nanjing University of Information Science and Technology Earth System Model version 3 (NESM v3) has been developed, aiming to provide a numerical modeling platform for cross-disciplinary Earth system studies, project future Earth climate and environment changes, and conduct subseasonal-to-seasonal prediction. While the previous model version NESM v1 simulates the internal modes of climate variability well, it has no vegetation dynamics and suffers considerable radiative energy imbalance at the top of the atmosphere and surface, resulting in large biases in the global mean surface air temperature, which limits its utility to simulate past and project future climate changes. The NESM v3 has upgraded atmospheric and land surface model components and improved physical parameterization and conservation of coupling variables. Here we describe the new version's basic features and how the major improvements were made. We demonstrate the v3 model's fidelity and suitability to address global climate variability and change issues. The 500-year preindustrial (PI) experiment shows negligible trends in the net heat flux at the top of atmosphere and the Earth surface. Consistently, the simulated global mean surface air temperature, land surface temperature, and sea surface temperature (SST) are all in a quasi-equilibrium state. The conservation of global water is demonstrated by the stable evolution of the global mean precipitation, sea surface salinity (SSS), and sea water salinity. The sea ice extents (SIEs), as a major indication of highlatitude climate, also maintain a balanced state. The simulated spatial patterns of the energy states, SST, precipitation, and SSS fields are realistic, but the model suffers from a cold bias in the North Atlantic, a warm bias in the Southern Ocean, and associated deficient Antarctic sea ice area, as well as a delicate sign of the double ITCZ syndrome. The estimated radiative forcing of quadrupling carbon dioxide is about 7.24Wm-2, yielding a climate sensitivity feedback parameter of -0:98Wm-2 K-1, and the equilibrium climate sensitivity is 3.69 K. The transient climate response from the 1%yr-1 CO2 (1pctCO2) increase experiment is 2.16 K. The model's performance on internal modes and responses to external forcing during the historical period will be documented in an accompanying paper. [ABSTRACT FROM AUTHOR]

Precipitation estimation with high-accuracy and spatiotemporal resolution is the key for distributed hydrological modelling. Gauge-satellite precipitation merging provides an effect way to improve the spatial precipitation estimation. In spite of the encouraging outcomes of gauge-satellite merging, it is quite a challenging job to derive precipitation appropriate for distributed watershed hydrological modeling when facing the following problems: (1) the spatial resolution of satellite-based precipitation product is much coarser than the modeled one; (2) the spatial distribution information of daily/sub-daily precipitation input to distributed hydrological models cannot be well captured by a single satellite-based precipitation product. In allusion to these problems, a two-step strategy for multi-source information merging was proposed in this paper to improve the spatial resolution and accuracy of daily precipitation estimates. The strategy is based on geographically weighted ridge regression kriging (GWRRK) with emphasis on dealing with non-stationarity and local collinearity in the regression context. Firstly, several individual satellite-based precipitation products, e.g., Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Aanlysis (TMPA) 3B42RT, Climate Prediction Center MORPHing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Global Satellite Mapping of Precipitation (GSMaP), were downscaled to 1km by employing the relationships between precipitation and other environmental factors such as topography and vegetation. And then, all these downscaled products were merged with gauge observations. Considering the strongly skewed distribution of daily precipitation, Box-Cox transformation was carried out prior to the GWRRK approach to give a more normal distribution. To our knowledge, this is the first research to downscale satellite-based precipitation product at daily scale using geographically weighted regression method and to merge gauge observations and multiple satellite-based precipitation products. The two-step merging strategy was applied to an experiment conducted in Xijiang Basin of China from 2010 to 2017. Results showed that: (1) the regression relationships in spatial downscaling and gauge-multisatellites merging were strongly spatially non-stationary; (2) local collinearity existed in both steps: this problem was not serious in spatial downscaling but serious enough to be the main barrier for the application of geographically weighted (GWR) in gauge-multisatellites merging; (3) the spatial trend of the downscaled data was consistent with that of the original data for all the four satellite-based daily precipitation products; (4) the merging of gauge observations and multiple satellite-based precipitation products provided more accurate estimates than the merging of gauge observations and a single satellite-based precipitation product. The GWRRK-based two-step merging strategy presented in this study has provided an efficient way to derive daily precipitation with high-accuracy and spatiotemporal resolution. [ABSTRACT FROM AUTHOR]