HRLT: A high-resolution (1 day, 1 km) and long-term (1961–2019) gridded dataset for temperature and precipitation across China

Abstract

Abstract. Accurate long-term temperature and precipitation estimates at high spatial and temporal resolutions are vital for a wide variety of climatological studies. We have produced a new, publicly available, daily, gridded maximum temperature, minimum temperature, and precipitation dataset for China with a high spatial resolution of 1 km and over a long-term period (1961 to 2019). It has been named the HRLT and the dataset is publicly available at https://doi.org/10.1594/PANGAEA.941329 (Qin and Zhang, 2022). In this study, the daily gridded data were interpolated using comprehensive statistical analyses, which included machine learning, the generalized additive model, and thin plate splines. It is based on the 0.5° × 0.5° grid dataset from the China Meteorological Administration, together with covariates for elevation, aspect, slope, topographic wetness index, latitude, and longitude. The accuracy of the HRLT daily dataset was assessed using observation data from meteorological stations across China. The maximum and minimum temperature estimates were more accurate than the precipitation estimates. For maximum temperature, the mean absolute error (MAE), root mean square error (RMSE), Pearson’s correlation coefficient (Cor), coefficient of determination after adjustment (R2), and Nash-Sutcliffe modeling efficiency (NSE) were 1.07 °C, 1.62 °C, 0.99, 0.98, and 0.98, respectively. For minimum temperature, the MAE, RMSE, Cor, R2, and NSE were 1.08 °C, 1.53 °C, 0.99, 0.99, and 0.99, respectively. For precipitation, the MAE, RMSE, Cor, R2, and NSE were 1.30 mm, 4.78 mm, 0.84, 0.71, and 0.70, respectively. The accuracy of the HRLT was compared to those of the other three existing datasets and its accuracy was either greater than the others, especially for precipitation, or comparable in accuracy, but with higher spatial resolution or over a longer time period. In summary, the HRLT dataset, which has a high spatial resolution, covers a longer period of time and has reliable accuracy, is suitable for future environmental analyses, especially the effects of extreme weather.

Latitudinal patterns of forest ecosystem stability across spatial scales as affected by biodiversity and environmental heterogeneity

“…Following White et al (2022), the climatic history was quantified based on the probability of the occurrence of extreme climate events. We extracted the daily temperature and daily precipitation measurements from 1961 to 2004 for the geographic coordinates of each plot using a gridded dataset with a resolution of 1 × 1 km (Qin & Zhang, 2022) (Appendix S5). Extreme precipitation and temperature were both defined by the “fat tail” measure, which represents the range of extreme climates relative to the central part of the data: , where represents the x quantile of the distribution (Schmid & Trede, 2003; White et al, 2022).…”

Impact of planting Lycium barbarum L. on the carbon–land–water nexus of a Haloxylon ammodendron nature reserve in the Qaidam basin, China

“…Plant growth was simulated in 1 × 1 km 2 grids over the study area. The meteorological input data for the model included daily maximum temperature, minimum temperature, and precipitation at a spatial resolution of 1 km between 2000 and 2019 (Qin & Zhang, 2022). The first 10 years were used to spin up the model and balance the soil nutrition pools, and the simulated results were for the period 2010–2019.…”

Future Land Use and Flood Risk Assessment in the Guanzhong Plain, China: Scenario Analysis and the Impact of Climate Change

“…The MAE, RMSE, Cor, R 2 , and NSE were 1.30 mm, 4.78 mm, 0.84, 0.71, and 0.70. The resolution of the dataset was 1 km × 1 km [48].…”