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The Qinghai‐Tibet Plateau (QTP) is highly susceptible to destructive rainstorm hazards and related natural disasters. However, the lack of sub‐daily precipitation observations in this region has hindered our understanding of rainstorm‐related hazards and their societal impacts. To address this data gap, a new approach is devised to estimate sub‐daily precipitation in QTP using daily precipitation data and geographical information. The approach involves establishing a statistical relationship between daily and sub‐daily precipitation based on data from 102 observation sites. This process results in a set of functions with six associated parameters. These parameters are then modeled using local geographical and climatic information through a machine learning algorithm called support vector regression. The results indicated that the temporal scaling characteristics of sub‐daily precipitation can be accurately described using a logarithmic function. The uncertainty of the estimates is quantified using the coefficient of variance and coefficient of skewness, which are estimated using a logarithmic and linear curve, respectively. Additionally, the six parameters are found to be closely linked to geographical conditions, enabling the creation of a 1‐km parameters data set. This data set can be utilized to quantitatively describe the probabilistic distribution and extract key information about maximum precipitation duration (from 1 to 12 hr). Overall, the findings suggest that the generated parameters data set holds significant potential for various applications, including risk analysis, forecasting, and early warning for rainstorm‐related natural disasters in QTP. The innovative method developed in this study proves to be an effective approach for estimating sub‐daily precipitation and assessing its uncertainty in ungauged regions. Plain Language Summary: As one of famous hotspots for natural disaster studies on Earth, the Qinghai‐Tibet Plateau (QTP) is highly vulnerable to destructive rainstorm hazard and related natural disasters, causing significant damage to property, infrastructure, agriculture, and resulting in extensive loss of life. Short‐duration heavy precipitation at sub‐daily scales is an important trigger for flash flood, debris flows and other disasters in QTP. However, it is a poorly gauged high mountain region, observed data for sub‐daily precipitation is extremely limited. Although there have been several satellite products and reanalysis data for sub‐daily precipitation in QTP, their quality has large bias and uncertainty compared to observations. It leaves a large data gap of sub‐daily precipitation, hindering the studies of rainstorm‐related natural disasters in the region. In this work, we develop a new strategy to quantify the temporal scaling characteristics of sub‐daily precipitation, as a basis of temporal downscaling. Then we use the new strategy to generate a parameters data set, to fill the data gap of sub‐daily precipitation in QTP. The parameters data set generated provides an effective way to estimate sub‐daily precipitation and its uncertainty, which can effectively serve for the rainstorm‐related natural disasters study in QTP. Key Points: A high‐resolution gridded parameters data set is generated to estimate sub‐daily precipitation and its uncertainty in QTPThe temporal scaling characteristics of sub‐daily precipitation in QTP is well described by a logarithmic functionSpatial heterogeneity in the temporal scaling characteristics of sub‐daily precipitation in QTP is closely related to geographical conditions [ABSTRACT FROM AUTHOR]

High Mountain Asia (HMA) is the headwater area for major Asian rivers, providing a vast amount of freshwater to billions of people in Asia. These rivers also make their surrounding areas highly vulnerable to destructive water‐related disasters. However, the complex spatiotemporal variability of runoff over HMA and its underlying mechanisms are poorly understood. This study investigates into the spatial heterogeneity of HMA's runoff variability at three timescales (interannual, interdecadal, and multidecadal) and the roles played by climate conditions and catchment properties. We find significant interannual and multidecadal variability of runoff in west and central HMA, and significant interdecadal variability in central and east HMA. At interannual and multidecadal timescales, the runoff variability tends to be more significant in dryer basins. The variability of runoff at the three timescales is largely controlled by climate variations, especially precipitation. The catchment properties, including groundwater storage and glacier‐snow meltwater, also play important roles in regulating the effect of precipitation. In particular, the high contributions of glacier‐snow meltwater in east HMA can weaken the response of runoff variability to precipitation at interannual and multidecadal timescales. The space‐time patterns of runoff variability over HMA are driven by atmospheric drivers including El Niño‐Southern Oscillation, Interdecadal Pacific Oscillation, and Atlantic Multidecadal Oscillation across timescales. The results of this study provide a better understanding of HMA's runoff variability and its physical mechanisms, which have critical implications for sustainable freshwater management and effective risk mitigation in this densely populated and ecologically vulnerable region. Key Points: Significant runoff variability at interannual and multidecadal timescales is detected in west and central High Mountain Asia (HMA)HMA's runoff variability is primarily controlled by precipitation, while catchment properties regulate precipitation effect in diverse waysSpatiotemporal variability in HMA's runoff is modulated by large‐scale atmospheric drivers including El Niño‐Southern Oscillation, Interdecadal Pacific Oscillation, and Atlantic Multidecadal Oscillation [ABSTRACT FROM AUTHOR]

Quantification of the dependence characteristics in hydrological time series is essential for understanding hydrological variability and for managing water resources. However, how determining a suitable model for describing the dependent components is still a challenge. In this article, we proposed a correlation coefficient‐based information criterion (CCIC) to determine the optimal model order of regression‐based models for quantifying the dependence characteristics in hydrological time series. CCIC was developed by combining the index of correlation coefficient and the information entropy index. The former was used to reflect the fitting error of the model and the latter was used as a penalty term to reflect the model uncertainty. They have similar roles as the two terms of the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) used commonly in hydrology. Results of Monte‐Carlo experiments verified higher and more stable accuracy of determining the true model order by CCIC than by AIC and BIC. Moreover, results indicated that the mean value of a time series had a big impact on the accuracy of CCIC. If the mean value of a time series was far from its initial value, the estimation of CCIC would have a big bias, causing low accuracy in the determination of suitable model order. The application of CCIC to annual precipitation from 520 stations in China further confirmed its advantages over AIC and BIC and indicated the significant short‐term dependence characteristics of annual precipitation in the Yangtze River basin. The proposed CCIC approach has potential for wider use in hydrometeorology. Key Points: Correlation coefficient‐based information criterion (CCIC) was proposed to determine the optimal order of regression models for quantifying dependence characteristics in hydrologic time seriesMC experiments verified higher and more stable accuracy of determining the true model order by CCIC than by Akaike Information Criterion and Bayesian Information Criterion used commonlyMean value of a time series had a big impact on the accuracy of CCIC for determining optimal order for regression models [ABSTRACT FROM AUTHOR]

Hot and dry extremes have co‐occurred with global warming, causing worse threats than single climate extremes. We analyse the compound hot and dry day (CHDD) and population exposure (Epop) across China based on observations and 11 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6. The bias‐corrected GCMs, particularly the two‐stage multivariate quantile mapping‐corrected ones, perform better than the raw ones in catching changes in CHDD but they all overestimate CHDD and Epop. More CHDDs occur in the southeast (SE), north (NC) and northwest (NW) China with increasing trends over regions stretching from the northeast (NE) to southwest (SW) China. Despite the declined CHDD, historical Epop has generally increased by the increased population. Future projections in the SSP5‐8.5 experiment indicate an overall increase of CHDD and Epop at 1.5°C warming and an increasing Epop to dangerous CHDD at 2°C warming. SW, NE and the Tibetan Plateau need effective measures of adaptation to reduce sufferings from CHDD under climate change. [ABSTRACT FROM AUTHOR]