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Due to limited direct measurements, regional or global terrestrial evapotranspiration (ET) is generally derived from a combination of meteorological and satellite observations. Although the inhomogeneity of the observed climate data has been widely reported, its impact on the calculated ET has not been adequately quantified. This study aimed to calculate ET using the modified Penman‐Monteith (MPM) model with raw and homogenized meteorological data. Additionally, we compared the calculated ET with those estimates from variable methods (water balance, satellite‐based, and reanalysis) in China and its six major river basins from 1982 to 2020. During the overlapping period of 1997–2018, ET calculated from raw input data decreased slightly at −0.39 mm yr−2(p= 0.64) in China, whereas homogenized ET showed a significant increasing trend of 0.93 mm yr−2(p= 0.02), with a better agreement with water balance ET (1.93 mm yr−2, p= 0). Global Land Evaporation Amsterdam Model (GLEAM) and Modern‐Era Retrospective Analysis for Research and Applications, version 2 (MERRA2) could reproduce the increasing trends with 2.08 mm yr−2(p= 0) and 2.59 mm yr−2(p= 0). The intercomparison of input variables (solar radiation, relative humidity, wind speed, precipitation, and air temperature) among ET products revealed substantial differences, which can account for the discrepancies in ET estimates. Homogenized ET, GLEAM and MERRA2 exhibited significant increasing trends in China and most river basins from 1982 to 2020. Our findings underscore the importance of utilizing homogenized input data for more accurate ET estimation. Regional or global terrestrial evapotranspiration (ET) is generally derived from a combination of meteorological and satellite observations. Previous studies have highlighted inhomogeneity issues in meteorological observations in China, which can introduce uncertainty to the ET estimated. However, the uncertainty of ET estimates has not been quantified. In this study, we recalculated ET in China using an energy partition model with raw and homogenized meteorological inputs from 1982 to 2020. We also compared ET estimates obtained from different methods (water balance, satellite‐based, and reanalysis) to analyze trends in China and its major river basins over the same period. The study revealed that inhomogeneity in the input data distorted the calculated trends of ET. Calculated China's ET using raw and homogenized data in the modified Penman‐Monteith modelImproved ET trend with homogenized data, aligning better with water balance ET over ChinaHomogenized ET exhibits a significant increasing trend of 0.90 mm yr−2(p= 0) from 1982 to 2020 across China Calculated China's ET using raw and homogenized data in the modified Penman‐Monteith model Improved ET trend with homogenized data, aligning better with water balance ET over China Homogenized ET exhibits a significant increasing trend of 0.90 mm yr−2(p= 0) from 1982 to 2020 across China

As part of a joint effort to construct an atmospheric forcing dataset for mainland China with high spatiotemporal resolution, a new approach is proposed to construct gridded near-surface temperature, relative humidity, wind speed and surface pressure with a resolution of 1 km×1 km. The approach comprises two steps: (1) fit a partial thin-plate smoothing spline with orography and reanalysis data as explanatory variables to ground-based observations for estimating a trend surface; (2) apply a simple kriging procedure to the residual for trend surface correction. The proposed approach is applied to observations collected at approximately 700 stations over mainland China. The generated forcing fields are compared with the corresponding components of the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis dataset and the Princeton meteorological forcing dataset. The comparison shows that, both within the station network and within the resolutions of the two gridded datasets, the interpolation errors of the proposed approach are markedly smaller than the two gridded datasets.

Terrestrial vegetation modulates the land‐atmosphere dynamics and is one of the fundamental forces for climatic variability. The vegetation feedback may enhance or offset a portion of ongoing global warming. However, it is challenging to disentangle the role of vegetation from the effect of other drivers, primarily due to lacking suitable reference without vegetation. Fortunately, China has recorded the land surface temperature (LST) on bare land in 2400s weather stations, providing a deal bare‐land reference. Here, we used satellite measurements of the vegetation's canopy temperature, contrasted with the surface temperature of bare land, to quantify the vegetation's effect on LST. Results show that vegetation has a cooling effect during daytime and a warming effect at night, and the magnitude of the former is significantly higher than that of the latter. Consequently, vegetation has a net cooling effect and reduces LST up to 2.18°C in China. Spatially, the cooling effect of vegetation decreases by 0.25°C for a unit increase of latitude. Based on land surface energy balance, the spatial pattern of vegetation's net effect is mainly driven by vegetation‐induced evapotranspiration and albedo. As latitude increases, the decrease of vegetation density significantly reduces the evapotranspiration‐induced cooling effect while having no distinct impact on the albedo‐induced warming effect. As a result, the net cooling effect of vegetation has an evident latitudinal gradient and a clear seasonal contrast. Additionally, the vegetation's effect on LST is sensitive to solar radiation and snow cover changes. Solar radiation is the primary energy source in weather and climate processes. Most solar radiation cannot be absorbed by the atmosphere directly, which passes through the atmosphere and is absorbed by land surface and then heats the atmosphere through sensible heat and longwave radiation. Vegetation covers most land surfaces and affects regional climate by regulating surface energy absorbing and partitioning. Due to the lack of non‐vegetation “scenario” as a reference, it is still difficult to accurately quantify the climate effects of vegetation. Fortunately, China takes the surface temperature of bare land as one of the routine observations in weather stations and has accumulated long‐term records. Here, we compared the satellite‐derived canopy temperature with the station‐observed bareland temperature to evaluate the vegetation's direct effects on regional climate. Further, we explored the physical mechanism for the spatial and seasonal variances. Results show that vegetation produced a cooling effect during daytime and reduced daily maximum temperature. The magnitude of the cooling effect had an obvious latitudinal pattern and seasonal contrast, which was more potent in low latitude (warm seasons) than high latitude (cold seasons). The spatial pattern of surface solar radiation and vegetation's biophysical effects determined the cooling effect's spatiotemporal pattern. We compared the satellite‐derived vegetation temperature with the station‐observed bareland temperature to evaluate the vegetation effectsVegetation has a net cooling effect at daytime while a warming effect at night, and has an apparent spatial pattern and seasonal contrastThe vegetation affects the surface energy budget by altering albedo and evapotranspiration, inhibits diurnal range of surface temperature We compared the satellite‐derived vegetation temperature with the station‐observed bareland temperature to evaluate the vegetation effects Vegetation has a net cooling effect at daytime while a warming effect at night, and has an apparent spatial pattern and seasonal contrast The vegetation affects the surface energy budget by altering albedo and evapotranspiration, inhibits diurnal range of surface temperature

Correctly estimating the forecast error covariance matrix is a key step in any data assimilation scheme. If it is not correctly estimated, the assimilated states could be far from the true states. A popular method to address this problem is error covariance matrix inflation. That is, to multiply the forecast error covariance matrix by an appropriate factor. In this paper, analysis states are used to construct the forecast error covariance matrix and an adaptive estimation procedure associated with the error covariance matrix inflation technique is developed.