An approach for analyzing unpredicted heat and precipitation events using spatiotemporal big data: A case study of Indian Western coastal cities

Climate change-related impacts have recently increased. Extreme precipitation and heat events are gaining attention because of their catastrophic damage, unpredictability and large spatial reach. Assessing such catastrophic events in a densely populated country such as India is critical because it exposes a large population to risk. This study is based on the approach that can be adopted to examine such unpredictable events. The whole-to-part approach is adopted, considering the region as a whole and urban areas as a part. This will provide a large spatiotemporal contextual understanding of a region, that must be considered when recommending mitigation strategies at the urban scale. The unpredicted future risk owing to unpredicted heat and precipitation events in the summer and winter seasons is analyzed using Coupled Model Intercomparison Project Phase 6 multimodel climate projections for the near future period 2020–2039 and the far future period 2080–2099 under Shared Socioeconomic Pathways for the entire region. This will reveal the spatial distribution of the potential risk of heat and precipitation events, guiding the formulation of policies at the municipal level in line with the severity of the potential risk event in that area. Furthermore, an analysis is conducted on the tier 2 cities in this region to identify and outline the vulnerable areas. It is essential to consider the regional context when implementing policies in this vulnerable area. By adopting this approach, it becomes possible to prevent both financial losses in investments and potential risks to human lives that may arise from insufficiently accurate predictions of such exceptional events. • This study focuses on an analytical approach employing a whole-to-part strategy for examining unpredicted precipitation and heat events using spatiotemporal big data. The Western Coastal Region of India is considered as a whole, while its tier 2 cities are considered as parts. • In the Western Coastal Region of India, the analysis utilizes the CMIP6 model to study unexpected precipitation and heat events during the summer and winter seasons for the periods 2020–2039 and 2080–2099. The findings reveal that the northern part of the study area is susceptible to heat events, while the southern part is prone to precipitation events. These insights are valuable for developing urban-scale policies. • When focusing on tier 2 cities in the Western Coastal Region, the analysis indicates that Surat has a higher percentage of areas vulnerable to extreme heat and precipitation events.