# Precipitation-Use Efficiency and Its Conversion with Climate Types in Mainland China
## 1 Introduction
At the same time, prior studies mostly focused on the effects of single climate factors on PUE, lacking a comprehensive understanding of the specific contributions of each climate factor and regional differences [16; 20; 21], which cannot fully reveal the difference and transformation characteristics of PUE with climate and vegetation-gradient distribution, thereby limiting the in-depth understanding of PUE characteristics and driving forces in different regions. NPP is a key for calculating the PUE. There are several methods for obtaining the NPP. In situ measurements have high data accuracy but are limited by the amount of data and are time-consuming and labor-intensive. Thus, they can only be used during surveys of small areas. Model estimation is an effective means of obtaining NPP on a regional or global scale. NPP estimation models can be roughly divided into three categories: ecophysiological process models, light-use efficiency models, and climate statistical models [23]. Ecophysiological process models simulate NPP based on the ecophysiological characteristics and growth mechanisms of plants [24]. Representative models include the Biome-BGC, CEVSA, and BEPS models. This type of model has strong mechanisms and is systematic. However, they are complex, and the required parameters are many and difficult to obtain. Light-use efficiency models use photosynthesis from vegetation and a resource balance view as the theoretical basis. They apply remote sensing data to drive ecological models for NPP simulation on regional or global scales and have been used worldwide. Representative models are the CASA and Glo-PEM models. Although this type of model has clear mechanisms and complete structures, the assignment and correction of model parameters are complicated, with large uncertainties. There are three main types of parameters based on the input of the light-use efficiency model: solar radiation data, maximum light-use efficiency, and environmental factors. The algorithms and required data for each parameter type are diverse. Different calculation methods have substantial differences in simulating vegetation NPP, especially in simulating environmental factors and maximum light use efficiency, because it is difficult to assign a large number of soil parameters to certain regions or special geomorphic types [25]. Additionally, these models are completely dependent on the availability and quality of remote sensing data [26]; in regions with strong spatial heterogeneity and complex terrain, the accuracy of the model is highly uncertain. Climate statistical models estimate NPP based on the correlation between plant growth and environmental factors. Representative models include the Miami, Thornthwaite memorial, Chicago, and comprehensive models. Although these models lack a mechanism, they are simple, intuitive, and highly applicable. Hence, they constitute the easiest and most convenient method for estimating NPP. Since their development in the 1970s, they have been applied in vegetation NPP research worldwide [20], particularly in relation to large-scale research. Various land-atmosphere mutual observation experiments also provide the possibility for model calibration, thereby continuously improving the accuracy of these models. In the context of global climate change, the characteristics and changes of China's regional PUE are not yet fully understood, as well as the regional differences and driving forces of the PUE response to climate change. What are the spatial distribution characteristics of the carbon flux in mainland China? How do environmental factors relate to PUE? Does PUE spatial conversion occur with climate type? What is the possible mechanism of action?