## 1 Introduction  
Terrestrial plants absorb carbon from the atmosphere through photosynthesis and store the carbon in biomass. Terrestrial gross primary production (GPP), the amount of carbon absorbed by terrestrial plants via photosynthesis, constitutes the largest CO\({}_{2}\) flux between the terrestrial biosphere and the atmosphere [1]. GPP drives the interannual variability of CO\({}_{2}\) fluxes [2; 3; 4], with important implications for the carbon-climate feedbacks [5]. Therefore, accurately quantifying GPP is critical for assessing ecosystem function, human welfare, and carbon balance. However, there is substantial uncertainty in the magnitude of GPP estimates at various spatial scales. Terrestrial biosphere and Earth system models (ESMs) exhibit substantial spread in the global GPP estimates. For example, the simulated present-day global GPP ranged from 105 to 177 Pg C yr\({}^{-1}\) based on a set of models in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) [6]. A more recent intercomparison study of eight terrestrial biosphere models showed that the simulated global annual GPP ranged from 98 to 141 Pg C yr\({}^{-1}\)[7]. The large biases in GPP can lead to even larger relative uncertainty in net ecosystem production (NEP), the difference between GPP and ecosystem respiration. Accurately quantifying GPP is therefore also essential for benchmarking terrestrial biosphere and ESMs and improved understanding of the land-atmosphere interactions. Over the recent decade, satellite-based measurements of solar-induced chlorophyll fluorescence (SIF) have provided an unprecedented opportunity for studying GPP at regional to global scales [8; 9; 10; 11; 12; 13]. SIF is directly coupled to the physiological processes of plants [14; 15], and can better diagnose the actual variations in photosynthesis than the conventional reflectance-based vegetation indices [16; 17; 18; 19; 20]. Several studies have shown the strong relationship between GPP from coarse-resolution gridded products and SIF from the Greenhouse Gases Observing Satellite (GOSAT) and the Global Ozone Monitoring Experiment 2 (GOME-2) [10; 11; 20; 21; 22]. These coarse-resolution SIF data (GOME-2: \(40\times 80\) km\({}^{2}\) and GOSAT: 10 km diameter) cannot be directly converted to GPP estimates due to the lack of reliable SIF-GPP relationships. Primary factors that could bias the SIF-GPP relationship include heterogeneity of both coarse-resolution SIF and GPP data, systematic errors in the gridded GPP products, and instruments degradation of GOME-2 [23]. The Orbiting Carbon Observatory-2 (OCO-2) launched in July 2014 has provided SIF retrievals with a much finer footprint (1.3 km \(\times\) 2.25 km) and higher data density than previous missions (e.g., GOSAT, GOME-2) [8]. The ground area of the OCO-2 soundings is close to the footprint of eddy covariance (EC) flux towers from which ground-based GPP (denoted as tower GPP henceforth) is estimated. Based on OCO-2 SIF and tower GPP, several pioneering studies demonstrated strong SIF-GPP relationship over different biomes [16; 17; 24; 25; 26; 27], suggesting that OCO-2 SIF has strong potential in deriving global GPP with higher resolution. However, although OCO-2 provides soundings with smaller size, its spatially and temporally sparse global coverage [17; 28] hinders direct generation of finer-resolution (e.g., \(0.05^{\circ}\) and 8-day) GPP maps based on these SIF-GPP relationships. To solve this problem, global continuous SIF data (\(0.05^{\circ}\)) have been recently developed from OCO-2 SIF based on machine learning methods [29; 30; 31]. Compared to the two studies solely based on surface reflectance from the Moderate Resolution Imaging Spectroradiometer (MODIS) [29; 30], [PERSON] and [PERSON] [31] used not only the enhanced vegetation index (EVI) derived from MODIS but also meteorological data (air temperature, photosynthetically active radiation or PAR, vapor pressure deficit or VPD) that can account for the regulation effects of environmental variables on SIF.