I want you to act as an academic journal editor. Your task is to follow the Reviewer Comment to rewrite a new conclusion based a old conclusion of paper.
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OLD CONCLUSION
Extreme precipitation events influenced by ENSO serve as the critical pathway by which ENSO impacts the worldwide climate. Specifically, the tropical Pacific precipitation induced by ENSO has the potential to trigger extreme weather occurrences globally, leading to substantial harm to agriculture, ecosystems, and economic stability. However, existing spatiotemporal clustering algorithms face challenges in effectively identifying rapidly changing sea surface precipitation anomaly variations. To address this issue, this paper proposes an object-oriented spatiotemporal clustering algorithm using raster (OSCAR) that can effectively identify precipitation anomaly variation patterns. The OSCAR was validated using simulated datasets and applied to identify sea surface precipitation anomaly variations during typical ENSO events in tropical Pacific. The relationship between these variations and ENSO events was analyzed. The main work in this paper is as follows: 
(1) Design of Spatiotemporal clustering algorithm 
A spatiotemporal clustering algorithm called Object-oriented Spatiotemporal Clustering Algorithm based Raster (OSCAR) is proposed to identify precipitation anomaly variations based on their evolution characteristics. The proposed algorithm involves a two-step approach, where spatial clustering is first performed on raster pixels of each timestamp to obtain segment objects. Subsequently, similar segments of adjacent timestamps are clustered to ensure the spatial consistency at each timestamp and the temporal continuity of spatiotemporal clusters. 
(2) Validation of Spatiotemporal clustering algorithm 
The OSCAR is validated using simulated datasets and compared with traditional DcSTCA. The results of the experiment show that OSCAR is more effective in identifying rapidly changing precipitation anomaly variations compared to DcSTCA. Additionally, the OSCAR exhibits higher accuracy than the DcSTCA, as evidenced by improvements in the ARI and NMI index of the clustering results from 0.88 and 0.81 to 0.95 and 0.91, respectively. This validation study demonstrates the effectiveness of OSCAR in identifying spatiotemporal clustering patterns of precipitation anomaly variations.
(3) Application of sea surface precipitation anomaly variations 
The OSCAR is utilized to the spatiotemporal clustering of ENSO-related sea surface precipitation anomaly in the tropical Pacific region. The experiment results indicate 2 key points. Firstly, positive anomaly clusters occur in the central-eastern tropical Pacific during El Niño events, while negative anomaly clusters occur in the western-central tropical Pacific during La Niña events. Secondly, the positive or negative anomalies enhance with El Niño or La Niña events. Previous research has demonstrated that during El Niño events (e.g., 2015), warm sea temperature anomalies result in increased precipitation in the central tropical Pacific Ocean; conversely, during La Niña events (e.g., 2010), negative precipitation anomalies coexist with low sea temperature anomalies in the central and western tropical Pacific Ocean [4, 16, 33]. The spatiotemporal clustering results presented in this study are comparable to the previous research based on static statistical values within a specific time frame. This illustrates the effectiveness of the OSCAR in detecting the evolution of ENSO-related precipitation anomalies, which may provide valuable reference for understanding the feedback and driving mechanism between tropical Pacific precipitation and ENSO.
Author Contributions: Conceptualization, Lianwei Li and Yuanyu Zhang; Data curation, Yuanyu Zhang and Lianwei Li; Funding acquisition, Lianwei Li and Cunjin Xue; Methodology, Yuanyu Zhang and Cunjin Xue; Software, Yuanyu Zhang; Validation, Yuanyu Zhang and Zhi Zheng; Writing—original draft, Yuanyu Zhang; Writing—review & editing, Lianwei Li and Yuanyu Zhang. All authors have read and agreed to the published version of the manuscript.
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COMMENT
The conclusion should not be repeat of the works of this study. But the state the most important outcome of your work, i.e. interpret your findings at a higher level of abstraction. Yet add some perspectives.
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New conclusion is