Air pollution over China has attracted wide interest from public and academic community. PM2.5 is the primary air pollutant across China. Quantifying interactions between meteorological conditions and PM2.5 concentrations are essential to understand the variability of PM2.5 and seek methods to control PM2.5. Since 2013, the measurement of PM2.5 has been widely made at 1436 stations across the country and more than 300 papers focusing on PM2.5-meteorology interactions have been published. This article is a comprehensive review on the meteorological impact on PM2.5 concentrations. We start with an introduction of general meteorological conditions and PM2.5 concentrations across China, and then seasonal and spatial variations of meteorological influences on PM2.5 concentrations. Next, major methods used to quantify meteorological influences on PM2.5 concentrations are checked and compared. We find that causality analysis methods are more suitable for extracting the influence of individual meteorological factors whilst statistical models are good at quantifying the overall effect of multiple meteorological factors on PM2.5 concentrations. Chemical Transport Models (CTMs) have the potential to provide dynamic estimation of PM2.5 concentrations by considering anthropogenic emissions and the transport and evolution of pollutants. We then comprehensively examine the mechanisms how major meteorological factors may impact the PM2.5 concentrations, including the dispersion, growth, chemical production, photolysis, and deposition of PM2.5. The feedback effects of PM2.5 concentrations on meteorological factors are also carefully examined. Based on this review, suggestions on future research and major meteorological approaches for mitigating PM2.5 pollution are made finally.

In response to a growing demand for subnational and spatially explicit data on China’s future population, this study estimates China’s provincial population from 2010 to 2100 by age (0–100+), sex (male and female) and educational levels (illiterate, primary school, junior-high school, senior-high school, college, bachelor’s, and master’s and above) under different shared socioeconomic pathways (SSPs). The provincial projection takes into account fertility promoting policies and population ceiling restrictions of megacities that have been implemented in China in recent years to reduce systematic biases in current studies. The predicted provincial population is allocated to spatially explicit population grids for each year at 30 arc-seconds resolution based on representative concentration pathway (RCP) urban grids and historical population grids. The provincial projection data were validated using population data in 2017 from China’s Provincial Statistical Yearbook, and the accuracy of the population grids in 2015 was evaluated. These data have numerous potential uses and can serve as inputs in climate policy research with requirements for precise administrative or spatial population data in China.

[1] Quantification of global land evapotranspiration (ET) has long been associated with large uncertainties due to the lack of reference observations. Several recently developed products now provide the capacity to estimate ET at global scales. These products, partly based on observational data, include satellite‐based products, land surface model (LSM) simulations, atmospheric reanalysis output, estimates based on empirical upscaling of eddy‐covariance flux measurements, and atmospheric water balance datasets. The LandFlux‐EVAL project aims to evaluate and compare these newly developed datasets. Additionally, an evaluation of IPCC AR4 global climate model (GCM) simulations is presented, providing an assessment of their capacity to reproduce flux behavior relative to the observations‐based products. Though differently constrained with observations, the analyzed reference datasets display similar large‐scale ET patterns. ET from the IPCC AR4 simulations was significantly smaller than that from the other products for India (up to 1 mm/d) and parts of eastern South America, and larger in the western USA, Australia and China. The inter‐product variance is lower across the IPCC AR4 simulations than across the reference datasets in several regions, which indicates that uncertainties may be underestimated in the IPCC AR4 models due to shared biases of these simulations.

[1] A global intercomparison of 12 monthly mean land surface heat flux products for the period 1993–1995 is presented. The intercomparison includes some of the first emerging global satellite‐based products (developed at Paris Observatory, Max Planck Institute for Biogeochemistry, University of California Berkeley, University of Maryland, and Princeton University) and examples of fluxes produced by reanalyses (ERA‐Interim, MERRA, NCEP‐DOE) and off‐line land surface models (GSWP‐2, GLDAS CLM/Mosaic/Noah). An intercomparison of the global latent heat flux (Q_le) annual means shows a spread of ∼20 W m⁻² (all‐product global average of ∼45 W m⁻²). A similar spread is observed for the sensible (Q_h) and net radiative (R_n) fluxes. In general, the products correlate well with each other, helped by the large seasonal variability and common forcing data for some of the products. Expected spatial distributions related to the major climatic regimes and geographical features are reproduced by all products. Nevertheless, large Q_le and Q_h absolute differences are also observed. The fluxes were spatially averaged for 10 vegetation classes. The larger Q_le differences were observed for the rain forest but, when normalized by mean fluxes, the differences were comparable to other classes. In general, the correlations between Q_le and R_n were higher for the satellite‐based products compared with the reanalyses and off‐line models. The fluxes were also averaged for 10 selected basins. The seasonality was generally well captured by all products, but large differences in the flux partitioning were observed for some products and basins.

Land evapotranspiration (ET) estimates are available from several global data sets. Here, monthly global land ET synthesis products, merged from these individual data sets over the time periods 1989–1995 (7 yr) and 1989–2005 (17 yr), are presented. The merged synthesis products over the shorter period are based on a total of 40 distinct data sets while those over the longer period are based on a total of 14 data sets. In the individual data sets, ET is derived from satellite and/or in situ observations (diagnostic data sets) or calculated via land-surface models (LSMs) driven with observations-based forcing or output from atmospheric reanalyses. Statistics for four merged synthesis products are provided, one including all data sets and three including only data sets from one category each (diagnostic, LSMs, and reanalyses). The multi-annual variations of ET in the merged synthesis products display realistic responses. They are also consistent with previous findings of a global increase in ET between 1989 and 1997 (0.13 mm yr⁻² in our merged product) followed by a significant decrease in this trend (−0.18 mm yr⁻²), although these trends are relatively small compared to the uncertainty of absolute ET values. The global mean ET from the merged synthesis products (based on all data sets) is 493 mm yr⁻¹ (1.35 mm d⁻¹) for both the 1989–1995 and 1989–2005 products, which is relatively low compared to previously published estimates. We estimate global runoff (precipitation minus ET) to 263 mm yr⁻¹ (34 406 km³ yr⁻¹) for a total land area of 130 922 000 km². Precipitation, being an important driving factor and input to most simulated ET data sets, presents uncertainties between single data sets as large as those in the ET estimates. In order to reduce uncertainties in current ET products, improving the accuracy of the input variables, especially precipitation, as well as the parameterizations of ET, are crucial.