# Improving the Gross Primary Production Estimate by Merging and Downscaling Based on Deep Learning
## 1 Introduction
LSM products are more consistent in spatiotemporal distribution and better in response to climate change, but various model parameters, input data, and model structures cause more significant uncertainty [15; 39; 40]. Especially in large-scale applications, the accuracy of the model is affected to some extent [41]. However, the current GPP products rely on assumptions. They are based on different estimation models so that no GPP product group can perform consistently better in diverse ecosystems and external conditions [42]. In particular, there is significant uncertainty in CMIP6 datasets under future climate change scenarios. The uncertainties in climate prediction are relevant to the modelling of GPP, although they are independent of the estimation method of GPP. As a result, the changes and trends obtained from different GPP products are often various [43], leading to unconvincing research results of a single GPP product [44]. Therefore, to overcome the limitations of a single dataset of GPP products, the question of how to effectively merge multiple datasets has been raised. Studies have shown that the multi-model ensemble mean is better than the single model in estimating GPP [17; 45]. Traditionally, the IPCC report shows that the average value of multiple models is the best. Empirical evidence from various modelling fields indicates that the multi-model ensemble mean eliminates at least some deviations from the single model, which can produce better predictions or values closer to observation [46; 47; 48; 49]. However, the simple multi-model ensemble mean depends on the assumption that the uncertainty of each dataset is the same [50]. However, this assumption is not reasonable from the differences between datasets. On the other hand, the spatial resolutions between prediction CMIP6 GPP datasets are generally low and mismatched. At present, bilinear interpolation is widely used for downscaling. Super-Resolution Convolution Neural Network [51] is a pioneering work of image super-resolution reconstruction. It makes people pay attention to the deep learning convolution neural network, the performance of which is superior to the traditional super-resolution. A neural network called Ynet [52] combines image super-resolution and data merging technology, which has been further used for soil moisture data merging with excellent results [53]. It establishes the relationship between reference data and multi-model datasets in the historical period to generate a merged GPP product with the distribution characteristics of reference data. Furthermore, GPP-merged data in the projection period under multiple climate scenarios can be obtained based on 23 CMIP6 model projections if the relationship built during the historical period is applied to the projection period. The evaluation in the historical period shows that the quality of the merged data is significantly improved compared with the multi-model ensemble mean, indicating that the merged data are reliable. Therefore, the variation trends in GPP under multiple climate scenarios in the future were further analyzed based on the merged data. This study aims to produce an improved high-resolution merged GPP dataset by adopting a new method to combine the advantages of 23 CMIP6 GPP datasets, without relying on any prior knowledge, to analyze the long-term variation trends in GPP under multiple climate scenarios in the future based on the improved merged data. Based on the historical CMIP6 GPP data, the deep learning method called Ynet [52] neural network is used to train the deep learning model with merging and downscaling functions. The 23 groups of monthly GPP datasets are integrated into this high-resolution long-series dataset called CMIP6 DL GPP. The period and the spatial resolution are 1850-2100 and 0.25 degrees. The future projection period includes four climate change scenarios, such as SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5.