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Estimating an accurate spatial distribution of precipitation with high resolution is necessary for hydrological and ecological applications, especially in data‐scarce and terrain‐complicated river basins. Satellite‐based precipitation data have been widely used to measure the spatial patterns of precipitation, but an improvement in accuracy and resolution is needed. In this article, a new statistical downscaling method is proposed to generate improved monthly precipitation fields at a higher spatial resolution of 1 km in Heihe River basin (HRB), China. The presented methods employed the geographical weighted regression (GWR) method to explore the non‐stationarity between precipitation and its factors, and used the high‐accuracy surface modelling method (HASM) to compensate for the errors produced in the GWR downscaling process. The GWR model was first established under five different spatial scales, and the optimal relation between precipitation derived from the Tropical Rainfall Measuring Mission (TRMM) and its influencing factors was found for each month. The errors caused during the scale change were modified by performing HASM as a data merging framework, which considered both the local climate characteristics and meteorological observations. Results showed that the GWR downscaling method could not generate spatial patterns of precipitation similar to those of the original TRMM products. Although the performance of the GWR method after residual interpolations using Kriging, IDW, and tension Spline was improved, there existed significant variations in some regions, and the accuracy of those methods was still not satisfactory. In comparison with the other four models, GWR‐HASM showed better performance in reproducing the precipitation field at a high spatial resolution. Results indicate that the proposed downscaling method appears feasible for precipitation estimation in data‐scarce river basins.

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The method of surface modeling of land cover scenarios (SMLCS) has been improved to simulate the scenarios of land cover in the karst areas of southwestern China. On the basis of the observation monthly climatic data collected from 782 weather stations of China during the period from 1981 to 2010, the climatic scenarios data of RCP26, RCP45 and RCP85 scenarios released by CMIP5, and the land cover current data of China in 2010, the land cover scenarios of southwestern China were respectively simulated. The average total accuracy and Kappa index of SMLCS are 90.25 and 87.96 %, respectively. The results show that there would be a very apparent similar variety on the spatial distribution pattern of land cover in the karst areas of southwestern China under all the three scenarios during the period from 2010 to 2100, but there would have the different change rate. In general, the change rate of land cover type under RCP85 scenario would be the fastest, then under RCP45 scenario, and under RCP26 would be the slowest. From 2010 to 2100, deciduous coniferous forest, deciduous broadleaf forest, grassland, cropland, nival area, and desert and bare rock would have a gradual decrease trend, while evergreen coniferous forest, evergreen broadleaf forests, mixed forest, scrublands, wetlands, construction built-up land, and water bodies body would gradually increase in karst areas of southwestern China, in which wetland would have the fastest increase rate (5.28 % per decade on average), and desert and bare rock would decrease with the fastest rate (2.34 % per decade on average).Keywords Surface modeling of land cover scenarios (SMLCS) Á Land cover Á Scenarios Á Karst areas of Southwestern China

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