Xuejia Wang

The Tibetan Plateau (TP), also known as the “Water Tower of Asia,” profoundly impacts regional and global climates. Existing climate models show substantial biases over the TP, primarily due to low spatial resolution, deficient driving data, and inadequate model domains. Leveraging meteorological station data, the CN05.1 meteorological dataset, and reanalysis datasets, we comprehensively evaluate the performance of the Weather Research and Forecasting Model at gray-zone resolution (9 km) (WRF9km) in simulating TP air temperature and precipitation during 1980–2019 and identify bias causes. WRF9km effectively captures observed spatial air temperature patterns but shows a cold bias. This is mainly due to overestimated surface albedo (accounting for 64%), along with underestimated downward radiation and ground heat fluxes. WRF9km also simulates observed spatial precipitation patterns well, with significant seasonal correlations; however, precipitation biases exhibit pronounced spatial heterogeneity. Compared to CN05.1, precipitation is overestimated over the southern and eastern slopes of the TP, while it is underestimated over the interior TP, especially in the western part. These biases primarily arise from inadequate characterization of wind field dynamics and moisture transport within the model framework. Meanwhile, WRF9km captures annual precipitation variation with minor deviations, although it does not fully reproduce the temporal trends over the TP. Overall, compared to driving ERA5 data, WRF9km yields only marginal improvement for the cold bias but reduces the regional mean precipitation wet bias by 79%, particularly over the southern and eastern slopes. This evaluation provides critical insights for dynamic downscaling in complex terrain, highlighting the need for improved surface albedo parameterizations and higher-quality input data.

Mountain regions are highly climate-sensitive, yet long-term observational evidence of elevation and seasonal climate dynamics in Central Asia remains limited. This study examines spatiotemporal trends in temperature (Tmean, Tmax, Tmin, and diurnal temperature range [DTR]) and precipitation across Kazakhstan’s transmountain regions using 74 meteorological stations (1981–2023). Data were analyzed using the Mann–Kendall test and Sen’s slope estimator, stratified across six elevation zones from lowlands (1500 m). Results reveal a robust, spatially coherent warming signal across all zones. Annual Tmean increased at a median rate of ~0.30 °C decade⁻¹, peaking at 0.36 °C decade⁻¹ above 1500 m, corresponding to an absolute increase exceeding 1.5 °C. Warming exhibited strong seasonal and diurnal asymmetries. Spring warmed most rapidly, with Tmean increasing >0.60 °C decade⁻¹ (approaching 3 °C total). Winter warming was driven by Tmin increases (up to 0.44 °C decade⁻¹), causing widespread DTR contraction, whereas summer warming was driven by Tmax increases, expanding DTR at higher elevations. Tmin showed the strongest elevation amplification overall. In stark contrast, precipitation trends were weak, spatially heterogeneous, and largely non-significant. Annual changes ranged from −6.63 to +14.35 mm decade⁻¹, with seasonal tendencies indicating modest, non-significant winter/spring wetting and summer drying. Ultimately, the results demonstrate a profound decoupling between strong, elevation-dependent warming and weak precipitation changes. The acute amplification of temperature, particularly during spring and summer at high elevations, has severe implications for snowmelt timing, glacier mass balance, evapotranspiration demand, and long-term water security in Kazakhstan.

The Tibetan Plateau (TP), also known as the “Water Tower of Asia”, profoundly impacts regional and global climates. Existing climate models exhibit substantial biases over the area, primarily due to low spatial resolution, deficient driving data, and inadequate model domains. Leveraging meteorological station data, the CN05.1 gridded meteorological dataset, and various reanalysis datasets, we comprehensively evaluate the performance of the Weather Research and Forecasting model at gray-zone resolution (9 km) (hereafter WRF9km) in simulating TP air temperature and precipitation during 1980—2019, and identify bias causes. WRF9km effectively captures the spatial patterns of observed air temperature, although it exhibits a cold bias that can mainly be explained by overestimated surface albedo (accounting for 64%), along with underestimated downward radiation and ground heat fluxes. WRF9km also simulates observed spatial precipitation patterns well (seasonal correlations > 0.5, significant at the 95% level); however, its precipitation biases exhibit pronounced spatial heterogeneity, with overestimation along the slope regions of the southern and eastern TP and underestimation over the interior TP, particularly the western TP, relative to CN05.1. These biases primarily arise from inadequate characterization of wind-field dynamics and moisture transport within the model framework. Meanwhile, the WRF9km effectively captures annual precipitation variation with minor deviations, although it does not fully reproduce the temporal trends in precipitation over the TP. Overall, compared to the driving ERA5 data, WRF9km yields only marginal improvement in mitigating the cold bias but substantially reduces the regional mean precipitation wet bias by 79%, particularly over the southern and eastern slopes. This evaluation provides critical insights to advance dynamic downscaling studies in complex terrain, highlighting the need for enhanced surface albedo parameterizations and improved quality of input driving data.

The Ili River Basin is characterized by complex topography and diverse climatic zones with limited in situ observations. This study evaluates the performance of six widely used precipitation datasets, CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), ERA5_Land (European Centre for Medium-Range Weather Forecasts—ECMWF Reanalysis 5_Land), GPCC (Global Precipitation Climatology Centre), IMERG (Integrated Multi-satellite Retrievals for GPM), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), and TerraClimate, against ground-based data from 2001 to 2023. The evaluation is conducted across multiple spatial scales and temporal resolutions. At the basin scale, most datasets exhibit strong correlations with in situ observations across all temporal scales (r > 0.7), except for PERSIANN, which demonstrates a relatively weaker performance during summer and winter (r < 0.6). All datasets except ERA5_ Land show low annual and monthly bias (<5%), although larger errors are observed during summer, particularly for IMERG and PERSIANN. Dataset performance generally declines with increasing elevation. Basin-wide gridded evaluations reveal distinct spatial variations across all elevation zones, with CHIRPS showing the strongest ability to capture orographic precipitation gradients throughout the basin. All datasets correctly identified 2008 as a drought year and 2016 as a wet year, even though the magnitude and spatial resolution of the anomalies varied among them. These findings highlight the importance of selecting precipitation datasets that are suited to the complex topographic and climatic characteristics of transboundary basins. Our study provides valuable insights for improving hydrological modeling and can be used for water sustainability and flood–drought mitigation support activities in the Ili River Basin.