2019
DOI: 10.5194/essd-11-1931-2019
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1 km monthly temperature and precipitation dataset for China from 1901 to 2017

Abstract: Abstract. High-spatial-resolution and long-term climate data are highly desirable for understanding climate-related natural processes. China covers a large area with a low density of weather stations in some (e.g., mountainous) regions. This study describes a 0.5′ (∼ 1 km) dataset of monthly air temperatures at 2 m (minimum, maximum, and mean proxy monthly temperatures, TMPs) and precipitation (PRE) for China in the period of 1901–2017. The dataset was spatially downscaled from the 30′ Climatic Research Unit (… Show more

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Cited by 773 publications
(259 citation statements)
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“…Various high-resolution climate datasets at the regional scale have been developed in many countries in recent years. These countries include China (Hong et al, 2005;Xie et al, 2007;Yatagai et al, 2009;Wu and Gao, 2013;Peng et al, 2019;Huang et al, 2020;Xu et al, 2020), India (Sinha et al, 2006), Southeast Asia (Van den Besselaar et al, 2017), Europe (Nynke et al, 2009), the United States (Price et al, 2004), Australia (Hutchinson, 1991), etc., which provide good supports to climate change research at regional scales. However, there exist some obvious difference in accuracy due to the different station number, data quality control and homogenization processing in the basic dataset used by each developer.…”
Section: Introductionmentioning
confidence: 99%
“…Various high-resolution climate datasets at the regional scale have been developed in many countries in recent years. These countries include China (Hong et al, 2005;Xie et al, 2007;Yatagai et al, 2009;Wu and Gao, 2013;Peng et al, 2019;Huang et al, 2020;Xu et al, 2020), India (Sinha et al, 2006), Southeast Asia (Van den Besselaar et al, 2017), Europe (Nynke et al, 2009), the United States (Price et al, 2004), Australia (Hutchinson, 1991), etc., which provide good supports to climate change research at regional scales. However, there exist some obvious difference in accuracy due to the different station number, data quality control and homogenization processing in the basic dataset used by each developer.…”
Section: Introductionmentioning
confidence: 99%
“…Annual temperature and precipitation data (~1 km resolution) between 1920 and 2017 over the QTP were derived from Peng et al (2019). This data set is spatially downscaled from the Climatic Research Unit (CRU) data, and shows good agreement with CMA weather stations across China between 1951 and 2016.…”
Section: Meteorological Datamentioning
confidence: 99%
“…From each plot, soil surface samples (0.5 liters; 10 cm of depth) were collected from three points and then combined into one composite (Harris et al, 2020). We used delta spatial downscaling method (details in Peng et al, 2019) and data from WorldClim 2.1 (Fick & Hijmans, 2017) to downscale the CRU data to 1 km 2 . The monthly corrections were applied to all months in each census interval.…”
Section: Environmental Datamentioning
confidence: 99%