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All provinces of China respond to the central government, predict future carbon dioxide emissions, and formulate action plans detailing how the province intends to fulfill its target of carbon emission peaking before 2030. Based on the bottom‐up energy consumption prediction and top‐down goal verification, this paper constructs a set of regional carbon dioxide emission prediction methods. Compared to the traditional bottom‐up prediction method, this method could simplify the parameters while improving the prediction accuracy. This model is used to predict and analyze the process of carbon dioxide emission peaking in Zhejiang. The results show that the mean absolute percentage error of the retrospective prediction value is only 1.56%. Zhejiang will reach carbon dioxide emission peaking around 2029–2030, and the peak value will be 569.7 million tons. Different factors have different effects on the process of carbon dioxide emission peaking. There is a strong correlation between the peak time of carbon dioxide emission and the production time of major energy‐consuming projects in Zhejiang. Meanwhile, if the 16 nuclear reactors are not put into operation, Zhejiang will not be able to achieve the goal of carbon dioxide emission peaking. Besides, the basic data used in this model is mainly from the local statistical departments of the region. Thus, it can be applied to other provinces and regions conveniently. [ABSTRACT FROM AUTHOR]

A high temporal–spatial resolution (1 hr/1 km) gridded temperature data set with a long time‐series (2006–2015) is established for Zhejiang province, China, which has a complex terrain, including plains, hills, basins, mountains, islands and oceans. A comprehensive analysis method is used to combine regional surface station data with reanalysis data from the European Centre for Medium‐Range Weather Forecasts (ECMWF) such that the observations at the stations are reproduced, and the reanalysis data provide the spatial structure for the interpolation. The elevation correction method and inverse distance‐weighted interpolation method are selected for comparison. The quality of the data set is evaluated and analysed by comparison with the cross‐validation results, third‐party data validation and other gridded data sets. The results show that the comprehensive analysis method provides the best outcome, followed by the inverse distance‐weighted method, and the reanalysis data elevation correction method has the worst result. The temperature error obtained from the gridding methods increases with altitude, decreases with increasing station density and varies with different synoptic conditions. The obtained data set can describe the temperature feature of peaks and valleys more precisely, which produces lower errors and higher spatial correlation co‐efficients compared with observation than the other gridded data sets. Therefore, the established data set can meet the needs of fine meteorological businesses and has potential applications in many fields. [ABSTRACT FROM AUTHOR]