Based on the rainfall station observations and the products of Multi-source Merged Precipitation Analysis System of China Meteorological Administration (CMPAS), eight kinds of satellite-based precipitation products (FY-4A, CMOPRH-RT, IMERG-Early, IMERG-Late, GSMaP-Now, GSMaP-Gauge, PERSIANN-Now, PERSIANN-CCS) are comprehensively evaluated during the recordbreaking extremely heavy precipitation process in East Gansu on July 15, 2022 by using quantitative analysis, classification and struc⁃ tural similarity methods. The results show that eight kinds of satellite-based precipitation products basically reflect the spatial distribu⁃ tion characteristics of precipitation with more in the central and eastern regions and less in the northwest. Except for the GSMaP-Now product, the other seven satellite-based precipitation products all underestimate the precipitation at the center of the rainstorm. The eight kinds of satellite-based precipitation products have a good ability to describe the peak value of heavy precipitation, and both peak stages of the heavy precipitation process are reflected, but all of them seriously underestimate the magnitude of heavy rainfall and above. For precipitation of different magnitudes, the GSMaP-Gauge is the best for estimating precipitation of magnitude below torrential rain, while the CMOPRH-RT is the best for heavy rain and above, and all products cannot correctly hit the precipitation of torrential heavy rainfall. In terms of the structural similarity index, the CMOPRH-RT product can best represent the structural distribution of the precipitation process from three aspects of total precipitation, precipitation magnitude, and precipitation morphological distribution. In summary, for this precipitation event, the CMOPRH-RT precipitation product had the best performance in all aspects.

The global climate model BCC-CSM2-MR (Beijing Climate Center-Climate System Model version 2-Medium Resolution) in⁃ dependently developed by the National (Beijing) Climate Center, which has participated in the Climate Model Interomparison Project Phase 6. Based on the BCC-CSM1.1m version, the BCC-CSM2-MR model is optimized in aspects of atmospheric radiation transport scheme, deep convection processes and gravity wave drag. Therefore, the improvement of the model’s ability to simulate precipitation and temperature in East Asia needs further assessment. Utilizing gridded observational datasets and station observations in China, the paper thoroughly compares the performances of BCC-CSM2-MR and BCC-CSM1.1m in simulating seasonal mean precipitation (tem⁃ perature) and daily precipitation (temperature) extremes in East Asia. The results are as follows: (1) Compared with the BCC-CSM1.1m, the BCC-CSM2-MR improves the model performance in simulating seasonal mean precipitation in most sub-regions of East Asia, espe⁃ cially for summer precipitation in the Tibetan Plateau. In particular, the model can better reproduce the annual cycle of precipitation in southeastern China, the Korean Peninsula and Japan. (2) The ability of the BCC-CSM2-MR to simulate the seasonal mean temperature in East Asia has not been improved significantly, and the simulated biases of monthly temperature change in most sub-regions of East Asia are greater than those of the BCC-CSM1.1m. (3) In terms of daily extreme precipitation (temperature), the simulation ability of the BCC-CSM2-MR is obviously better than that of the BCC-CSM1.1m, which significantly improves the simulation ability of daily extreme precipitation (temperature) in southeast China. Overall, the improvement of the BCC-CSM2-MR in deep convection process parameter scheme is beneficial to the simulation of precipitation in East Asia.

It is difficult to forecast heavy precipitation under complex terrain in mountainous areas， which formation mechanism is complicated， and often brings serious geological disasters. Based on conventional observation data， European Centre for MediumRange Weather Forecasts ERA5 reanalysis data， FY-4A satellite cloud imagery， Doppler radar data and forecast products from various models， the factors contributing and model forecasting performance of local short-time heavy precipitation process in the Hanjiang Basin of southern Shaanxi from the night on 3 to the early morning on 4 June 2022 were examined and analyzed. The results are as follows：（1） This process is a short-time heavy precipitation triggered by the front in the Hanjiang Basin of southern Shaanxi. Due to shallow convection instability and weak vertical wind shear， the heavy precipitation exhibited localized characteristics with significant intensity. The accumulated precipitation in 12 hours exceeds 50 mm in many stations， with a maximum of 104. 8 mm. （2） The two ends of the front are blocked by the topography and move slowly and are difficult to cross the high mountains. Consequently， convection is continuously triggered within the basin， generating heavy precipitation， and the secondary circulation formed in the surface layer of the basin can enhance convective activity. （3） A cold pool formed in the front of front continuously triggers the backward propagation of new convective cells within the basin to form a train effect. Meanwhile， the intense radar reflectivity factor， exceeding 50 dBZ， is located below the 0 ℃ isotherm level， which has high precipitation efficiency and prolonged duration， thus bringing a short-time heavy precipitation with a maximum of 62. 6 mm·h-1 . （4） Global models displayed limited capability in forecasting this process， while mesoscale regional models can reflect the characteristics of frontal convection and precipitation， especially CMA-TRAM and CMA-GD models can reflect the triggering and development trend of local strong convection well. However， the intensity and organization of the convective system induced by the frontal cold pool of the front still have substantial forecast deviations.

The TCIs (tourism climate indices) are applications of the human comfort in climate health tourism. TCIs aim to characterize the climate and environmental status of tourist destinations and provide a comprehensive measure of tourists’ climatic well-being. In this study, the Web of Science and China National Knowledge Infrastructure (CNKI) databases were searched for visualization analysis, explained the connotation of the TCIs and human comfort index, summarized the historical evolution of TCIs and the human comfort index, and discussed the existing problems and countermeasures of TCIs researches. In order to provide theoretical support for the indepth research and sustainable development of TCIs in the future. The results are as follows: (1) The English studies were more concerned with tourism satisfaction and competitiveness, whereas the Chinese studies were more concerned with human comfort, climate comfort, and climate tourism. (2) Human comfort indices were primarily composed of meteorological factors and clothing insulation, which aims to reflect the human body's feeling of environmental comfort. The TCIs not only covers meteorological factors and aesthetic factors, but also considers health factors such as air quality and oxygen content, aiming to comprehensively evaluate the suitability of tourism activities and the effect of climate health of travelers.

Exploring the quantitative impact of short-term weather variability intensity (SWVI) on influenza incidence in Hubei Prov⁃ ince is of significant importance for conducting early risk warning and formulating prevention policies. Based on the influenza inci⁃ dence data and meteorological station observation, an index of SWVI has been built, which can measure the cumulative changes over a short-term in minimum temperature between two consecutive weeks. Based on the Distributed Lag Nonlinear Model (DLNM), the rela⁃ tion between SWVI index and influenza incidence risk was evaluated and a set of method for level classification of influenza incidence risk was developed. The results show that the intra-annual variation of number of Influenza-Like Illnesses (ILI) exhibited bimodal struc⁃ ture, with the first peak occurring in autumn and winter, and the second peak appearing in early summer months. The SWVI index also exhibited a bimodal distribution, but the peak occurring earlier than the peak of ILI. From November to March of the following year, SWVI index had a strong indicative significance for the change of ILI morbidity. In this period, when SWVI reaches 8.0 ℃, the cumula tive relative risk (RR) of ILI incidence at the same period and the next week was 1.16 (95% confidence interval: 1.087-1.250). In addition, SWVI index also had an indirect effect on the risk of ILI with a lag of 4-9 weeks, which was less affected than the immediate effect, but lasted longer. Using the percentile method and the relationship model between the SWVI index and the ILI incidence risk, a set of influenza risk early warning method was established. When the SWVI index was greater than or equal to 8.0 ℃, the influenza incidence reached high risk level

Sandstorm is a serious natural disaster in north China. It is of great significance to carry out relevant research to improve the forecast level of this kind of catastrophic weather. Based on the RegCM-dust model, an extended period numerical prediction analysis of a typical severe sandstorm process in north China is conducted, and the results are compared with NCEP reanalysis data and other analysis results. The results show that the regions with high sediment discharge simulated by the model are mainly located in southern Xinjiang, Mongolia and western Inner Mongolia. The model has a certain forecasting ability for 10 m wind speed, but the simulated wind speed is smaller than the reanalysis data. The changes of dust column content and total sedimentation simulated by the model can reflect the characteristics of the dust storm weather process. The simulated sand-dust mixing ratio has a certain correspondence with the urban pollution index, which indicates that the model has certain forecasting ability for the pollution weather caused by sand-dust.

Arranging 3 years’ worth of airborne precipitation particle images to construct a precipitation particle image dataset in Shan⁃ dong Province. Building a precipitation particle recognition model based on EfficientNet convolutional neural network, named PREN (Precipitation particle Recognition model based on EfficientNet convolutional neural Network).The accuracy rate is 98%, and the multimodel and multi-index evaluation and comparison experiments verify that PREN demonstrates excellent robustness and generalization ability. Taking typical stratiform-cumulus mixed cloud precipitation as two examples (total 3 time periods), PREN is applied to the par⁃ ticle characteristics analysis of generating cells. Combined with airborne Ka-band cloud radar and DMT particle measurement system, an analysis conducted on the shape proportion of precipitation particles inside and outside the generating cells and indifferent intensity generating cells, revealing the precipitation mechanism. The results show that the shapes of precipitation particles in the generating cells are mainly spherical, needle-like, irregular and columnar. Precipitation particles outside the generating cells are mostly spherical and needle-like. The cloud microphysical parameters in the generating cells with different intensities vary. The proportion of graupel and needle particles in the precipitation maturity stage is higher than that in the dissipation stage. The average chord length of precipi⁃ tation particles in the maturity stage is 415 µm. While the average chord length of particles in dissipation stage is 367 µm. The par⁃ ticles on the top of generating cells are mainly spherical and hexagonal, primarily growing through the process of deposition. The ratio of irregular particles and columnar particles in the 0 ℃ are increasing, and the melting process and dynamic conditions favor aggregation and growth, forming irregular particles, while columns mainly originate from the upper levels of the atmosphere.

Analysis of circulation and impact of extreme weather processes is the basis of refined disaster prevention and mitigation services. Based on meteorological observation data, reanalysis data and satellite data, the characteristics, atmospheric circulation background and its main impacts of the extreme heatwave event in the Sichuan Basin from 25 July to 9 August 2021 are analyzed. During this heatwave event, temperatures of 13 national meteorological stations broke the historical maximum temperature records, and high temperature days reached 14 days at six stations. The heatwave center was located in the central and southern parts of the Sichuan Basin, and the process intensity reached its peak in early August, and the daily maximum temperature (42.4 ℃) appeared at Xingwen station in Yibin. The analysis shows that the atmospheric circulation background of this heatwave is different from that of most previous heatwave processes, the direct role of the western Pacific subtropical high (subtropical high) during this process is not obvious, and the typhoon activity in the southeast coast prevents the westward extension of the subtropical high. The peripheral flow of the subtropical high is conducive to the maintenance of the anticyclonic system over the basin, and makes it difficult for water vapor from the south to reach the basin, which plays an important role in the development and maintenance of high temperature and heatwave. During this heatwave process, the average high temperature days in Chengdu reached 8.36 days and the heat island effect is significant. The impact of heatwave and urban heat islands effect on mega-cities like Chengdu worths attention.