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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.

Coastal El Niño is an extreme situation of El Niño‐Southern Oscillation (ENSO) with sea surface temperature warming confined in the far‐eastern equatorial Pacific. Some coastal El Niños evolve into a basin scale El Niño, and some don't, implying a diversity in ENSO evolutions after a coastal El Niño event. In this study, the coastal El Niños in 2017 and 2023 are selected to examine their subsequent evolution. Both coastal El Niños developed after a La Niña, with the former followed by a La Niña and the latter by a basin‐scale El Niño. The cold (warm) subsurface temperatures in 2017 (2023) were key factors leading to the divergent ENSO evolution. Convection over the western tropical Pacific and the atmospheric circulation anomalies across the equatorial Pacific also contributed to the differences. Model predictions suggest that differences in ENSO evolution after a coastal El Niño are associated with differences in ENSO predictability. Plain Language Summary: Compared with the global impact of basin‐scale El Niño–Southern Oscillation (ENSO) events, coastal El Niño impacts are mainly focused along the South American coast. They are less studied, especially, in terms of temporal evolution and longer‐term development. Here, we examine the divergent evolution of ENSO conditions in the tropical Pacific after the coastal El Niños in 2017 and 2023. This subsequent divergent evolution of these events was associated with both preceding subsurface ocean heat content levels, convection over the western tropical Pacific, and concurrent atmospheric circulation. Specifically, preceding subsurface ocean cooling combined with low‐level easterly wind anomalies led to the growth of La Niña after the coastal El Niño in 2017, while strong preceding subsurface ocean warming led to the growth of El Niño after the coastal El Niño in 2023. These differences in the evolution of tropical Pacific Ocean conditions after a coastal El Niño were associated with different levels of ENSO predictability. Key Points: The coastal El Niño in 2017 was followed by a La Niña, while the coastal El Niño in 2023 evolved into a basin‐scale El NiñoSubsurface ocean heat content levels, western Pacific convection, and off‐equatorial circulation differences affect El Niño‐Southern Oscillation (ENSO) evolutionDivergent evolution of conditions in the tropical Pacific after a coastal El Niño is associated with differences in ENSO predictability [ABSTRACT FROM AUTHOR]