## 1 Introduction  
Net primary productivity (NPP) is the net amount of carbon accumulated by plants in a given period, and has been regarded as one of the main components of the carbon cycle [1]. Due to a variety of direct and/or indirect anthropogenic activities (e.g., land clearing and conversion) and nature disturbances (e.g., fire, pests) as well as global and regional climate change, forested ecosystems have undergone substantial changes in cover and have increasingly shown declines in health over recent decades [2, 3]. As a sensitive indicator of forest cover, function and health, NPP loss may affect the composition of the atmosphere, fresh water availability, biodiversity [4, 5], and the ecological adjusting mechanism of energy supply and distribution [6]. Quantifying the inter-annual variability in NPP and the interactions with climate factors would help us understand the terrestrial carbon dynamics and underlying mechanisms in responses to climate change [7]. Recently, many studies have been undertaken on the spatiotemporal variation of NPP and its relationship with climate factors on global and regional scales using a range of approaches from observational [2, 8, 9] to a suite of remote sensing based methods [10, 11]. However, the NPP results were diverse in trends and magnitudes, even in the same region among models [10, 12] due to different inputs. Moderate Resolution Imaging Spaectroradiometer (MODIS) annual NPP products have been widely used [13; 14]; however, uncertainties from inputs and the algorithm resulted in biases and restrained data use regionally or globally to some extent. For instance, 70-80% accuracy of MODIS land cover products (MOD12Q1) are an assimilated meteorological dataset, not observed data with coarse spatial resolution, the cloud-contaminated MODIS FPAR/LAI (MOD15A2), and weaknesses in the MOD17 algorithm [15; 16]. Therefore, it is essential to compare MODIS derived NPP to other models and/or field observed NPP, especially for China, which encompasses a wide range of ecosystems and climates. In addition, the responses of different ecosystems to different magnitudes of climate change are still far from clear, especially in China [17]. Moreover, few studies [12] have investigated the effect of solar radiation as an integrated surrogate for the effects of both day length and sunlight intensity [18] on vegetation NPP, especially when temperature and precipitation are considered simultaneously. In addition, previous studies have shown that vegetation phenology, an important factor that affects plant productivity, has changed dramatically due to climate changes and anthropogenic interference [19], but only a few studies have explored the effect of phenophase variation on NPP [20]. At present, due to the diversity of the trends and magnitudes of vegetation phenophases at different scales, its effects on NPP are still unclear, especially in China, where the vegetation phenophases are diverse at both the regional and biome levels [19]. Furthermore, a linear regression method has been applied by most studies to analyze NPP trend [10; 21] despite the trend always showing a non-linear trend, or has one or more turning points within the time period [22; 23]. These limitations and/or gaps have impeded our understanding of the dynamic relationship and consequently researchers may have underestimated future changes in plant productivity and/or the carbon cycle throughout China. China encompasses a variety of ecosystems and climates. The regional climate ranging from tropical to cold-temperate, and from humid in the south to extremely dry in the northwest [24]. Land cover types are diverse, including a broad range of tropical, temperate and boreal forests, grassland, cropland and desert [25]. In recent decades, China has experienced dramatic changes in climate such as remarkably strong El Nino events [26], the freezing low temperatures in early 2008 [24], and frequent occurrences of severe droughts [27].