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Climate dynamic diagnosis and numerical simulation are important means to understand the rules of climate variability and improve the service efficiency of short-term climate prediction and scientific decision-making. However, the dynamic diagnosis technology based on climate simulation has not been widely used in routine climate service, and almost no platforms can transform scientific research results into the use in climate operation conveniently. Therefore, by integrating various technologies such as modern computer communication protocols, visual editing and meteorological numerical simulation, Climate Dynamic Diagnosis and Analysis System (CDDAS) is developed, which can promote the dynamic diagnosis technology of climate simulation to be more widely used in climate operation. The system has the features with opening structure, high integration of diagnosis methods and high usability. Four functional modules are developed, including data management, climate dynamic diagnosis, multi-model numerical simulation and result analysis. Also, an interactive controlling language is designed, which can provide an easy method for user's further development. In the system, a communication toll among local PC (personal computer) client, remote server and supercomputer is built, which can be managed visually. Visual editing and management functions are provided to users to edit or design the interactive operation interfaces between local terminal and remote server, so as to provide online services according to their own needs. The script language provided by the system can control the visual buttons on the operation interface, the cloud computing in remote server and data network transmission, and it supports four arithmetic operations, logical judgment, numerical circulation and other statements, integrates a variety of network communication protocols, and provides a series of drawing, string processing, and window display control functions. Assisted programming and a fine interface designing tool is also provided. The client of the system can help users to manage interactive pages and graphics, and can make comparative analysis of climate diagnosis results. The system lays a good foundation for the automation of dynamic climate diagnosis. In particular, the establishment of multi-model numerical simulation module and the realization of visualization operation provide an effective way for the dynamic diagnosis and numerical simulation of climate models to be used in climate operation departments. At present, the system has been used in the national climate operation and scientific research units, which has significantly improved the efficiency and convenience of climate operation in the diagnosis of climate anomalies, climate prediction and climate decision-making services.

Anomalous summer rainfall in China is affected by many factors, whose complex interaction restricts the predictability of Chinese summer rainfall (CSR). The predicting skill of the state-of-the-art dynamic models on the CSR is still limited, leaving challenges in developing objective statistical predicting methods. A method for searching potential predicting skill of predictors (i.e., potential skill map, PSM) is proposed, which can be used to select predictors automatically based on the PSM, and a new automatic statistical prediction model of the CSR is established.Compared with traditional linear correlation analysis, the PSM using the cross-validation concept not only reflects the potential predicting skills of predictors on predictands, but is free from effects of extreme events. It is completely based on real-time statistical predicting procedure, which aims to find sufficient conditions for predictands in logical. The PSM is an important supplement to the traditional correlation coefficient map. They work together to provide potential predictors with necessary and sufficient conditions. The predictor automatic selector takes advantage of the idea of ensemble forecasting. It selects predictors with the most significant potential forecasting skill from the PSM, and then generates final forecast products by averaging a large number of predicting members. The year-by-year automatic selection of the predicators is thus realized. This solution doesn't rely on subjective experiences of foreasters, and also provides a new way to further investigate the predictability of the interannual variability of the East Asian summer monsoon. This new automatic statistical prediction model of the CSR based on the PSM and the predictor automatic selector shows a high reforecast skill for the CSR. In the 21-year reforecasting experiment from 1999 to 2019, predictors in the previous autumn and winter seasons are used to predict the CSR. Results show an average symbol agreement rate of 60% and the mean anomaly correlation coefficient of 0.436 between the reforecast and the observed CSR. As to the predicting skill (PS) score in the National Climate Center, the reforecast CSR reaches 71.00 in average. After variance correcting, the PS score further increases to 82.10, which is much higher than predicting skills of current dynamical models. It is noteworthy that the reforecast experiment in the present uses the first 12 multiple regression coefficients and EOF modes of the CSM, of which the first 4 multiple regression coefficients and EOF modes play a dominant role in the overall distribution of the CSM. By contrast, higher-order modes could further improve the reforecast skill by increasing the diversity of the reforecasting CSM, which represent their potential physical implications.

Recent years have witnessed contrasting trends in summer total rainfall (STR) over the Tibetan Plateau (TP), with an increase in the northern and a decrease in the southern TP. This study identifies four significant centers of rainfall trends: eastern TP ("region A"), Qiangtang Plateau ("B"), Qaidam Basin ("C"), and the northern foothills of the Himalayas ("D"). Heavy rainfall dominates STR trends in regions A and D, accounting for 55.6% and 52.0%, respectively. In region B, moderate and light rainfall contribute almost equally, accounting for 37.3% and 44.8% of the STR trend, respectively. Region C is primarily influenced by light rainfall, explaining 71.2% of the STR trend. Notably, the contributions of different rainfall intensities to STR in each region vary annually, with region A experiencing more heavy rainfall, region B having moderate dominance but less light rainfall, and region C and D showing reduced and increased light rainfall contributions, respectively. Mechanistically, the strengthening of the upper-level westerly jet and the South Asian High, coupled with changes in moisture transport and convective available potential energy, collectively cause variations in rainfall intensity, characterizing the spatial heterogeneity in STR in the TP. [ABSTRACT FROM AUTHOR]