Urbanization and global warming are the two major drivers of the warming environment in cities. The contributions of urbanization and global warming to the past occurrence of human‐perceived heatwaves (HWs) over China are evaluated in this study. Both observations and model simulations show HWs have become more intensive, longer‐lasting, and more frequent in recent decades. Urbanization and greenhouse gases contribute to 21.9% and 72.9% of the intensification of HWs, respectively. The occurrence probability of observed human‐perceived HWs has doubled over 1961–2012 and is projected to be about 4.36/5.92 times under the 1.5/2°C warming relative to the preindustrial level. At the 3.5°C warming, the average duration of HWs is projected to increase to 43.63 days/year, and the occurrence probability is expected to increase by 11.95 times, 91% of which is attributable to human‐induced warming. The highest sensitivity of the increases in HWs due to human‐induced warming is in Southern China.

Under global warming, projected changes in precipitation have shown an asymmetrical shift from light to heavy precipitation over China. However, the role of urbanization in this shift remains unknown. Here, we show that increases in total rainy‐season (May‐September) precipitation over the Yangtze River Delta (YRD) urban agglomeration of East China are characterized by decreasing light precipitation and increasing heavy precipitation during 1961–2019. This asymmetrical shift toward heavier precipitation is even more prominent in urban than rural areas. Areas with faster urban expansion rates exhibit stronger negative (positive) trends in light (heavy) precipitation. Urbanization contributes to 44.4% (26.4%) of the decreasing (increasing) light (heavy) precipitation in the urban areas of the YRD. We suggest that urban managers should consider potential adverse impacts of this asymmetrical shift, which may favor increases in both the frequency of heatwaves and waterlogging.

Xinjiang is the largest arid inland region in China and its agricultural development plays an important role in the socio‐economy of China. Drought is one of the major natural hazards in this region and has impacts on agriculture. In this study, spatiotemporal patterns of droughts are analysed based on monthly precipitation datasets from 52 meteorological stations covering the period of 1961–2008 using the Rotated Empirical Orthogonal Functions (REOF) technique and also the Mann–Kendall trend test (MK test). Results of the study indicate that: (1) a wetting tendency is observed in recent years, particularly in northern Xinjiang. Seasonally, the wetting tendency is found mainly in winter. The modes of REOF can be divided by the Tianshan mountain and the principal component (PC) of the northern Xinjiang mode in winter has a significant increasing trend. (2) The area of drought‐affected regions is decreasing in northwestern Xinjiang and along the northern side of Tianshan mountain. Seasonally, the area of drought‐affected regions is significantly decreasing in winter. (3) The wetting tendency of Xinjiang may alleviate the drought influence. However, uneven spatial patterns of precipitation changes may enhance drought impacts. Besides, drought‐related impacts can be greatly alleviated where irrigation conditions are favourable.

The partitioning of precipitation into runoff (R) and evapotranspiration (E), governed by the controlling parameter in the Budyko framework (i.e., n parameter in the Choudhury and Yang equation), is critical to assessing the water balance at global scale. It is widely acknowledged that the spatial variation in this controlling parameter is affected by landscape characteristics, but characterizing its temporal variation remains yet to be done. Considering effective precipitation (P_e), the Budyko framework was extended to the annual water balance analysis. To reflect the mismatch between water supply (precipitation, P) and energy (potential evapotranspiration, E₀), we proposed a climate seasonality and asynchrony index (SAI) in terms of both phase and amplitude mismatch between P and E₀. Considering streamflow changes in 26 large river basins as a case study, SAI was found to the key factor explaining 51 % of the annual variance of parameter n. Furthermore, the vegetation dynamics (M) remarkably impacted the temporal variation in n, explaining 67 % of the variance. With SAI and M, a semi-empirical formula for parameter n was developed at the annual scale to describe annual runoff (R) and evapotranspiration (E). The impacts of climate variability (P_e, E₀ and SAI) and M on R and E changes were then quantified. Results showed that R and E changes were controlled mainly by the P_e variations in most river basins over the globe, while SAI acted as the controlling factor modifying R and E changes in the East Asian subtropical monsoon zone. SAI, M and E₀ have larger impacts on E than on R, whereas P_e has larger impacts on R.