Remote sensing techniques provide data on the spatial–temporal distribution of environmental parameters over regions with sparse ground observations. However, the resolution of satellite precipitation data is too coarse to be applied to hydrological and meteorological research at basin scales. Downscaling research using coarse remote sensing data to obtain high-resolution precipitation data is significant for the development of basin-scale research. Here, we propose improvements to a spatial–temporal method for downscaling satellite precipitation. The improved method uses a nonlinear regression model and introduces longitude and latitude based on processed normalized difference vegetation index (NDVI) and a digital elevation model (DEM) to stimulate precipitation in the Qilian Mountains during 2006–2015. The final downscaled annual precipitation (FDAP) results are corrected by observed data to obtain corrected final downscaled annual precipitation (CFDAP) datasets. For temporal downscaling, monthly downscaled data are the corrected monthly ratio multiplied by the corresponding downscaled annual datasets. The results indicated that processed NDVI (PNDVI) reflected spatial precipitation patterns more accurately than the original NDVI. The accuracy was significantly improved when the final downscaled annual precipitation data were corrected by observed data. The average annual root mean square error (RMSE) from 2006 to 2015 of CFDAP was 66.48 and 83.07 mm less than that of FDAP and original Tropical Rainfall Measuring Mission (TRMM) data, respectively. Compared with previous methods, which use NDVI and/or DEM to downscale TRMM, the accuracy of FDAP and CFDAP from the improved method was higher, and the RMSE decreased on average by 13.63 and 80.11 mm. The RMSE of monthly data from corrected monthly ratio (CMR) decreased on average by 4.93 mm over monthly data from previous monthly ratio (PMR). In addition, the accuracy of the original satellite data affected the initial downscaling results but had no significant effects on the corrected downscaling results.

The Qilian Mountains (QMs) act as the “water tower” of the Hexi Corridors, playing an important role in the regional ecosystem security and economic development. Therefore, it is of great significance to understand the spatiotemporal characteristics of precipitation in the QMs. This study evaluated the performance of 21 models of phase 6 of the Coupled Model Intercomparison Project (CMIP6) from 1959 to 1988 based on ERA5 and in situ datasets. In addition, the precipitation changing trend from 2015 to 2100 was projected according to four shared socioeconomic pathways (SSPs): namely, SSP126, SSP245, SSP370, and SSP585. The results have shown the following: (1) all CMIP6 models could reflect the same precipitation changing trend, based on the observed datasets (−2.01 mm·10a⁻¹), which was slightly lower than that of ERA5 (2.82 mm·10a⁻¹). Multi-mode ensemble averaging (MME) showed that the projected precipitation-change trend of the four scenarios was 5.73, 9.15, 12.23, and 16.14 mm·10a⁻¹, respectively. (2) The MME and ERA5 showed the same precipitation spatial pattern. Also, during the period 1959–1988, the MME in spring, summer, autumn and winter was 130.07, 224.62, 95.96, and 29.07 mm, respectively, and that of ERA5 was 98.57, 280.77, 96.85, and 22.64 mm, respectively. The largest precipitation difference in summer was because of strong convection and variable circulation. (3) From 2015 to 2100, the snow-to-rain ratio was between 0.1 and 1.1, and the snow-to-rain ratio climate tendency rate was concentrated in the range of −10~0.1 mm·10a⁻¹. Both of these passed the significance test (p < 0.05). The projected rainfall of all four SSPs all showed an increasing trend with values of 6.20, 11.31, 5.64, and 20.41 mm·10a⁻¹, respectively. The snowfall of the four SSPs all showed a decreasing trend with values of 0.42, 2.18, 3.34, and 4.17 mm·10a⁻¹, respectively.

High spatial and temporal resolution products of near-surface air temperature (T2m) over the Greenland Ice Sheet (GrIS) are required as baseline information in a variety of research disciplines. Due to the sparse network of in situ data on the GrIS, remote sensing data and machine learning methods provide great advantages, due to their capacity and accessibility. The Land Surface Temperature (LST) at 780 m resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) and T2m observation from 25 Automatic Weather Stations (AWSs) are used to establish a relationship over the GrIS by comparing multiple machine learning approaches. Four machine learning methods—neural network (NN), gaussian process regression (GPR), support vector machine (SVM), and random forest (RF)—are used to reconstruct the T2m at daily and monthly scales. We develop a reliable T2m reconstruction model based on key meteorological parameters, such as albedo, wind speed, and specific humidity. The reconstructions daily and monthly products are generated on a 780 m × 780 m spatial grid spanning from 2007 to 2019. When compared with in situ observations, the NN method presents the highest accuracy, with R of 0.96, RMSE of 2.67 °C, and BIAS of −0.36 °C. Similar to the regional climate model (RACMO2.3p2), the reconstructed T2m can better reflect the spatial pattern in term of latitude, longitude, and altitude effects.

Solar radiation plays an important role in the cryospheric water cycle, especially in alpine regions. This study presents an evaluation of the Modern-Era Retrospective analysis for Research and Applications 2 (MERRA2), ERA5, High Asia Refined analysis version 2 (HAR v2), JRA-55, and National Centers for Environmental Prediction/Climate Forecast System Reanalysis datasets at different time scales by comparing observed datasets from July 2010 to December 2015 at 4,550 m in the Laohugou Basin. In terms of shortwave radiation, ERA5 performs significantly better than the other reanalysis radiation datasets. For downward shortwave radiation, HAR v2 performs better than ERA5 on only two timescales, 3 months and half-year, with mean absolute errors (MAEs) of 13.28 and 7.96 w/m². The upward shortwave radiation, ERA5, outperforms the other reanalysis datasets on all 12 timescales. For downward longwave radiation, ERA5 also performs significantly better, with only MERRA2 outperforming ERA5 on the daily scale and annual scale, with R², bias, root mean square error, and MAE of 0.6, 0.95, −9.51 w/m², −9.41 w/m², 34.98 w/m², 9.46 w/m², and 27.52 w/m², 9.41 w/m², respectively. In the upward longwave radiation, HAR v2 performs better than the other reanalysis datasets on all timescales, except for ERA5, which has a better R² of 0.92 on the annual scale. All the reanalysis datasets can show the variation trend of the four radiation parameters in different seasons and achieve a better performance in winter. Therefore, ERA5 is recommended for regions without shortwave radiation observations, and HAR v2 and ERA5 are recommended for longwave radiation simulations. Although there are obvious shortcomings in the reanalysis radiation datasets, they still provide important supplementary information for research in high-altitude areas, where the observed datasets are too sparse.