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The fragile ecological environment of the Mongolian Plateau (MP) is sensitive to climate change. It is necessary to fully understand the temperature and precipitation changes on the MP to ensure regional sustainable development. Most existing studies are conducted based on station observations which suffer from sparse distribution and limited spatial representativeness and cannot perfectly represent the climatic conditions of the whole region. By contrast, the long‐term, spatially continuous reanalysis products offer new opportunities for MP climate change research. However, the coarse spatial resolution and large uncertainties of reanalysis data limit their applicability to provide reliable climate information at finer scales. Within the delta downscaling framework, we downscale and correct the state‐of‐the‐art European Centre for Medium‐range Weather Forecasts ReAnalysis 5 land portion (ERA5‐Land) using a high‐resolution WorldClim reference climatology and generate a long‐term (1950–2020) 1‐km monthly air temperature and precipitation downscaled dataset. During the downscaling process, the results obtained by nearest neighbour, bilinear and bicubic interpolation methods are evaluated and compared. Air temperature result of bilinear method and precipitation result of nearest neighbour method, which have the best accuracy, are taken as the final downscaled data. The evaluation shows that the downscaled data has acceptable accuracy (overall MBE = −0.41°C, Corr = 0.997, RMSE = 1.51°C for air temperature, and MBE = 1.58 mm·month−1, Corr = 0.747, RMSE = 24.24 mm·month−1 for precipitation), and outperforms the original ERA5‐Land and widely used Climatic Research Unit (CRU) data. In addition, the downscaled data can also provide accurate and detailed temperature and precipitation change trends over the MP. With fine spatial resolution, long‐time span and good spatial continuity, the downscaled air temperature and precipitation will be a useful data source to explore climate change over the MP. [ABSTRACT FROM AUTHOR]

Temperature is the most concerned factor in the human–environment interactions. Apparent temperature accounting for other meteorological variables, such as humidity, wind speed and solar radiation, is the equivalent temperature, and it is a more accurate indicator to reflect human's environmental temperature perception. High‐quality apparent temperature data are urgently needed for the further research on human–environment interactions. At the same time, as global heat waves continue to increase in frequency, duration and intensity, understanding the impact of heat waves on human health needs human perception‐based heat wave data. Using ERA5 hourly data on single levels of 2 m temperature, wind speed, dewpoint temperature and solar radiation, this study developed a global apparent temperature and heat wave (GATHW) toolbox based on the Climate Data Store (CDS) online platform. This toolbox allows using three methods to calculate daily apparent temperature and heat wave at three spatial resolutions of 0.25°, 0.5° and 1° respectively. It can realize online calculation, display and real‐time download and is updated in near real‐time. The global daily apparent temperature and annual heat wave dataset from 2006 to 2020 calculated by the toolbox can be obtained from https://www.doi.org/10.5281/zenodo.4764325. After evaluation, this dataset can well reflect the typical extreme temperatures and heat wave events, and is more accurate, with higher resolution and faster update frequency than similar data products, which can provide data support for the study of human–environmental interactions and extreme climate events. [ABSTRACT FROM AUTHOR]

Although numerous satellite-based soil moisture (SM) products can provide spatiotemporally continuous worldwide datasets, they can hardly be employed in characterizing fine-grained regional land surface processes, owing to their coarse spatial resolution. In this study, we proposed a machine-learning-based method to enhance SM spatial accuracy and improve the availability of SM data. Four machine learning algorithms, including classification and regression trees (CART), K-nearest neighbors (KNN), Bayesian (BAYE), and random forests (RF), were implemented to downscale the monthly European Space Agency Climate Change Initiative (ESA CCI) SM product from 25-km to 1-km spatial resolution. During the regression, the land surface temperature (including daytime temperature, nighttime temperature, and diurnal fluctuation temperature), normalized difference vegetation index, surface reflections (red band, blue band, NIR band and MIR band), and digital elevation model were taken as explanatory variables to produce fine spatial resolution SM. We chose Northeast China as the study area and acquired corresponding SM data from 2003 to 2012 in unfrozen seasons. The reconstructed SM datasets were validated against in-situ measurements. The results showed that the RF-downscaled results had superior matching performance to both ESA CCI SM and in-situ measurements, and can positively respond to precipitation variation. Additionally, the RF was less affected by parameters, which revealed its robustness. Both CART and KNN ranked second. Compared to KNN, CART had a relatively close correlation with the validation data, but KNN showed preferable precision. Moreover, BAYE ranked last with significantly abnormal regression values. [ABSTRACT FROM AUTHOR]

Chronic obstructive pulmonary disease (COPD) is a major and increasingly prevalent respiratory health problem worldwide and the fine particulate matter (PM2.5) is now becoming a rising health threat to it. This study aims to conduct a comparison analysis of health effect on acute exacerbation of COPD (AECOPD) associated with PM2.5 exposure in two typical cities (Beijing and Shenzhen) with different levels of PM2.5 pollution. Both correlational relationship and causal connection between PM2.5 exposure and AECOPD are investigated by adopting a time series analysis based on the generalized additive model (GAM) and convergent cross mapping (CCM). The results from GAM indicate that a 10 μg/m3 increase in PM2.5 concentration is associated with 2.43% (95% CI, 0.50–4.39%) increase in AECOPD on Lag0-2 in Beijing, compared with 6.65% (95% CI, 2.60–10.87%) on Lag0-14 in Shenzhen. The causality detection with CCM reveals similar significant causative impact of PM2.5 exposure on AECOPD in both two study areas. Findings from two methods agree that PM2.5 has non-negligible health effect on AECOPD in both two study areas, implying that air pollution can cause adverse consequences at much lower levels than common cognition. Our study highlights the adverse health effect of PM2.5 on people with COPD after exposure to different levels of PM2.5 and emphasizes that adverse effect in area with relative low pollution level cannot be overlooked. Governments in both high-pollution and low-pollution cities should attach importance to the adverse effects of PM2.5 on humans and take corresponding measures to control and reduce the related losses. [ABSTRACT FROM AUTHOR]