Glacial lake outburst floods (GLOF) hazards in alpine areas are increasing. The effects of climate change on GLOF hazards are unclear. This study examined 37 glacial lakes and climate data from 15 meteorological stations and explored the correlation between climate variations at different temporal scales. The results indicate that 19 GLOFs hazards occurred in El Niño (warm) years, 8 GLOFs hazards occurred in La Niña (cold) years, 3 GLOFs hazards occurred in cold/warm or warm/cold transition years, and 7 GLOFs hazards occurred in normal years. The higher the fluctuations, the higher the probability of GLOF hazards. Climatic conditions can be divided into three categories: extreme temperature and precipitation, as represented by the Guangxie Co GLOF; extreme precipitation, as represented by the Poge Co GLOF; and extreme temperature, as represented by the Tsho Ga GLOF.

This study evaluates the abilities of fifteen High-resolution Coupled Model Intercomparison Project phase 6 (CMIP6) models to simulate temperature and precipitation over the Tibetan Plateau (TP) for the years 1980–2014. The impacts of terrain correction and Empirical Orthogonal Function (EOF) correction on simulations of temperature and precipitation are examined. The results show that equal-weighted ensemble averaging of the CMIP6 high-resolution model provides a good representation of the spatial distribution of temperature over the TP, although simulations underestimate observations by 1.87 °C. The simulated spatial range of temperature cooling significantly exceeds the observed range, particularly in the central and southwestern TP. The performances of the simulations for precipitation are far poorer than those for temperature, and although the CMIP6 model represents the distribution of annual mean precipitation, simulations of precipitation show significant deviations from observations. Furthermore, model simulations of precipitation are 1.57 mm lower than observed, and 30% lower than observed in the southeastern TP. However, the CMIP6 model overestimated the intensity of precipitation in most regions, especially in the southeastern part of the TP. Meanwhile, the EOF analysis indicates that the effects of the correction of temperature exceed that of precipitation. Therefore, a range of methods should be considered for correcting temperature and precipitation over a complex terrain.

The southeast region of Tibet experiences frequent glacial lake outburst disasters, and disaster warning systems are thus crucial for disaster prevention and mitigation in the area. In this study, based on remote sensing images and historical data, 20 glacial lakes in southeast Tibet were selected as samples for risk analysis. A probability model of glacial lake outburst floods (GLOFs) in southeast Tibet was established using logistic regression for seven selected prediction indexes. By calculating the sensitivity and specificity of the model, the probability of identifying GLOFs was found to be 60%, with an identification degree of 86%. The under the ROC (receiver operating characteristic) curve index was prominently larger than 0.5, indicating the applicability of logistic regression for predicting GLOFs in southeast Tibet. The probability equation of the model shows that the area of the glacial lake, the distance of the glacial lake from the glacier, the slope of the glacier, the slope of the glacier tongue, and the dam backwater slope have a great influence on the probability of GLOFs. The results can provide a reference for the local governments to prevent disasters and reduce the damage of GLOFs.

In this paper, the glacial debris flow blockage event, on 17 October 2018, in the Sedongpu basin of the Yarlung Zangbo River is taken as an example to analyse the occurrence and development of glacier hazards in this region. Multi-sources including remote sensing products, DEM, earthquake records and meteorological data were used to analyse the characteristics and mechanism of glacier hazards. The Elmer/Ice dynamic model was chosen to simulate the glacial surface velocity. It was found that topography and climate background determine that the hazard happens periodically. Based on the meteorological records of the Linzhi station, the warming rate was greater than 0.40 °C/10a during the period 1960–2017. The short-term heavy rainfall with daily values of 9.3 mm before the blockage event was also regarded as a factor. Both heavy rain and earthquake were triggering factors of the ice avalanche that led to the glacial debris flow. The glacier surface velocity of the Dongpu glacier simulated by Elmer/Ice model can reach 19 cm/d. This study has extensive applicability significance in glacier hazard mitigation under a changing climate.

The daily precipitation data of 1959-2017 years from May to September, with GPD and maximum likelihood method to under the laws and calculate the reappearance period values of heavy precipitation in Tibet. The results indicate that the daily precipitation of 28 stations in Tibet basically conforms to the model, and the theoretical frequency and the measured frequency are basically consistent. According to the spatial distribution of precipitation maximum value, the extreme values of Shigatse and Lhasa had large fluctuations, and the probability of record-breaking precipitation events was small. Precipitation extremes in the western part of Naqu region were relatively small, and the probability of record-breaking precipitation events was relatively high. The peak value of precipitation extremes in the flood season in Tibet generally showed a decreasing law from southeast to northwest, and the extreme value of the flood season reappeared in the southeast region was about twice that of the northwest region. The maximum rainfall in most areas will exceed 20mm in the next 5-10 years, and the maximum rainfall in Shigatse will reach 52.7 mm. After 15 years of recurrence in various regions, the peak rainfall growth in the flood season has become very slow.

Accurate characterization of precipitation in complex terrain is essential for hydrological modeling and climate studies. This study uses daily observations from 156 rain gauges in Sichuan Province (2015–2020) to evaluate two high-resolution satellite products (GSMaP-GNRT and IMERG-Early) and to develop a Transformer-based fusion framework at the gauge scale. All three datasets reproduce the regional seasonal cycle with more rainfall in summer and less in winter. At the daily scale, the fused product attains correlation comparable to GSMaP, while GSMaP and the fusion slightly overestimate precipitation (Bias = 6.24% and 5.21%), and IMERG shows stronger underestimation (Bias = −11.46%). At the monthly scale, the fused dataset achieves the best overall performance in terms of correlation, bias and RMSE. Spatially, the fusion reduces bias and RMSE and yields more homogeneous patterns over Sichuan’s complex terrain. Detection metrics indicate that the fused product increases the probability of detection and slightly improves the critical success index, while the false alarm ratio remains relatively high and comparable to the original products. This implies a gain in event sensitivity and spatial consistency rather than substantially reduced false alarms. Overall, the Transformer-based fusion provides a useful compromise between GSMaP and IMERG, adding value particularly for bias reduction, monthly statistics and event detection. The fused dataset offers a promising input for precipitation monitoring, hydrological simulation and disaster-risk analysis in Sichuan and similar mountainous regions.