Debris flows can develop into mega catastrophes in semi-arid regions when the source materials come from landslides, and both snowmelt and precipitation are involved in increasing water discharge. In such environments, the formation of large-scale debris flows exhibits a distinguishable pattern, in which a multi-fold lower triggering rainfall threshold holds compared to humid regions. Previous research mainly focuses on mechanisms in humid environments or neglects variations across aridity classes. In this study, the formation and evolutionary mechanism of a debris flow occurring in a semi-arid context is investigated via field surveys, granularity measurement, terrain and climate analyses, and snow cover change detection. By examining the July 22, 2021, Xiao Dongsuo debris flow at Amidongsuo Park in the Qilian Ranges on the northeastern margin of the Tibetan Plateau, the mechanism of debris flows in semi-arid regions is revealed. The research finds that the large debris flow, whose course erosion scales up the disaster by 0.12 million m3, is primarily supplied by landslide deposits of 1.16 million m3. The debris flow is empowered by the integrated flow of extreme precipitation and extreme heat-stimulated snowmelt. However, the precipitation required to trigger the debris flow is much lower than that of precipitation-dominated ones and those in humid regions. In semi-arid mountains, prolonged extreme heat tends to increase soil moisture in areas covered by snow or permafrost. This reduces slope stability and induces slope failures, amplifying the disaster magnitude and raising disaster risks through extended deterioration. Hence, this study inspects the failure mechanism associated with debris flows in semi-arid regions for a more comprehensive understanding to constitute viable control plans for analogous disasters.

The risk of floods has increased in South Asia due to high vulnerability and exposure. The August 2022 Pakistan flood shows a glimpse of the enormity and devastation that can further rise under the warming climate. The deluge caused by the floods in 2022, which badly hit the country's southern provinces, is incomparable to any recent events in terms of the vast spatial and temporal scale. The flood event is ranked second in human mortality, while this was the top event that displaced about 33 million people in Pakistan. Using observations and climate projections, we examine the causes and implications of the 2022 flood in Pakistan. Multiday (& SIM;15 days) extreme precipitation on wet antecedent soil moisture conditions was the primary driver of the flood in August 2022. The extreme precipitation in August was caused by two atmospheric rivers that passed over southern Pakistan. Streamflow simulations from the multiple hydrological models show that multiday extreme precipitation was the primary driver of floods. Several flood-affected stations experienced anomalously higher flow than the upstream stations. The 2022 Pakistan flood highlights the adaptation challenges South Asia is facing along with the substantial need for climate mitigation to reduce the risk of such events. Plain Language Summary The Pakistan flood of 2022 received a considerable attention. However, the causes and implications of the events have not been examined. Using observations, satellite data, and reanalysis products, we show that the event was caused by multiday extreme rainfall on wet antecedent conditions. The extreme rainfall was associated with the two atmospheric rivers that transported significant moisture from the Arabian Sea. The flood was primarily driven by the extreme precipitation and other factors (glacier-melt) played a secondary role. Extreme precipitation is projected to increase in a warming climate, which highlight the strong need of adaptation and mitigation.

Despite the importance of the Yellow River to China, climate change for the middle reaches of the Yellow River Basin (YRB) has been investigated far less than for other regions. This work focuses on future changes in mean and extreme climate of the YRB for the near-term (2021-2040), mid-term (2041-2060), and far-term (2081-2100) future, and assesses these with respect to the reference period (1986-2005) using the latest REgional MOdel (REMO) simulations, driven by three global climate models (GCMs) and assuming historical and future [Representative Concentration Pathway (RCP) 2.6 and 8.5] forcing scenarios, over the CORDEX East Asia domain at 0.22 degrees horizontal resolution. The results show that REMO reproduces the historical mean climate state and selected extreme climate indices reasonably well, although some cold and wet biases exist. Increases in mean temperature are strongest for the far-term in winter, with an average increase of 5.6 degrees C under RCP 8.5. As expected, the future temperatures of the warmest day (TXx) and coldest night (TNn) increase and the number of frost days (FD) declines considerably. Changes to mean temperature and FD depend on elevation, which could be explained by the snow-albedo feedback. A substantial increase in precipitation (34%) occurs in winter under RCP 8.5 for the far-term. Interannual variability in precipitation is projected to increase, indicating a future climate with more extreme events compared to that of today. Future daily precipitation intensity and maximum 5-day precipitation would increase and the number of consecutive dry days would decline under RCP 8.5. The results highlight that pronounced warming at high altitudes and more intense rainfall could cause increased future flood risk in the YRB, if a high GHG emission pathway is realized.

Heavy precipitation events are increasingly concerned because their significant contribution to annual precipitation in the Northwestern China, which might be related to invasion of summer monsoon moisture. It is interest whether or not the same is Jade Pass as being outside the control of the Asian summer monsoon. In this work, six heavy precipitation events were selected based on the 95 percentiles of the daily precipitation at the 12 weather stations around the Jade Pass from 1970-2000, with consideration of the influences of elevation. The event on June 19th, 2013 was chosen for a detailed examination due to the fact that the day has a large-scale precipitation as revealed by a gridded precipitation dataset over a large region. Using a Weather Research and Forecasting Model (WRF) simulation with high spatiotemporal resolution and in situ isotopic tracing (delta O-18, delta D), under a large-scale heavy precipitation event, this study provides ambitious view at the synoptic scale. A dramatic decrease in the delta O-18, delta D and deuterium (d)-excess of precipitation, very high relative humidity (98%), and reduced air temperature indicate that the precipitation was a result of long-distance-transported monsoon vapor. In addition, the slope of the local water meteoric line (LWML) of the precipitation for this event was very close to that of the global meteoric water line (GWML), indicating the source of moisture was from the ocean. Meanwhile, the WRF simulation confirms that the precipitation at the Jade Pass was not caused by local convection, but by summer monsoon. Both WRF simulation and isotopic tracing support the view that the monsoon moisture could invade Jade Pass at the synoptic scale and impact on precipitation, which need be further investigated.

Accurate estimates of extreme precipitation events play an important role in climate change studies and natural disaster risk assessments. This study aimed to evaluate the capability of the China Meteorological Forcing Dataset (CMFD), Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), and Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) to detect the spatiotemporal patterns of extreme precipitation events over the Qinghai-Tibet Plateau (QTP) in China, from 1981 to 2014. Compared to the gauge-based precipitation dataset obtained from 101 stations across the region, 12 indices of extreme precipitation were employed and classified into three categories: fixed threshold, station-related threshold, and non-threshold indices. Correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), and Kling-Gupta efficiency (KGE), were used to assess the accuracy of extreme precipitation estimation; indices including probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) were adopted to evaluate the ability of gridded products' to detect rain occurrences. The results indicated that all three gridded datasets showed acceptable representation of the extreme precipitation events over the QTP. CMFD and APHRODITE tended to slightly underestimate extreme precipitation indices (except for consecutive wet days), whereas CHIRPS overestimated most indices. Overall, CMFD outperformed the other datasets for capturing the spatiotemporal pattern of most extreme precipitation indices over the QTP. Although CHIRPS had lower levels of accuracy, the generated data had a higher spatial resolution, and with correction, it may be considered for small-scale studies in future research.

Investigation of extreme precipitation events in arid and semiarid regions, especially for occurrence time and the associated circulation mechanisms, is vital to support the forecasting of and the advanced response to resultant disasters. In this study, the spatiotemporal pattern of occurrence time of extreme precipitation and atmospheric circulation mechanisms in the Arid Region of Northwest China (ARNC) were analyzed using two indicators (precipitation concentration degree and period) and the climate diagnosis method. Results showed that the significant scattered pattern of extreme precipitation occurrence time (EPOT) in Northern Xinjiang and the postponed pattern of maximum extreme precipitation occurrence (MEPO) from southern to northern Xinjiang are consistent with the input pathway of the Arctic air mass. During the anomaly dispersion year of EPOT and the anomaly delay year of MEPO, the Arctic air mass carried sufficient water vapor is transported to ARNC for triggering extreme precipitation events. Meanwhile, the pattern of concentration-dispersion-concentration in eastern ARNC demonstrates interaction between the westerlies and the summer monsoon. Sufficient water vapor is transported to southwestern ARNC by the southwest monsoon during the anomaly delay year of MEPO and the anomaly concentration year of EPOT. The findings of this study suggest that invasion of the Arctic air mass and the summer monsoon could influence extreme precipitation in ARNC.