Worldwide, humans are facing high risks from natural hazards, especially in coastal regions with high population densities. Rising sea levels due to global warming are making coastal communities' infrastructure vulnerable to natural disasters. The present study aims to provide a coupling approach of vulnerability and resilience through restoration and conservation of lost or degraded coastal natural habitats to reclamation under different climate change scenarios. The integrated valuation of ecosystems and tradeoffs model is used to assess the current and future vulnerability of coastal communities. The model employed is based on seven different biogeophysical variables to calculate a natural hazard index and to highlight the criticality of the restoration of natural habitats. The results show that roughly 25% of the coastline and more than 5 million residents are in highly vulnerable coastal areas of mainland China, and these numbers are expected to double by 2100. Our study suggests that restoration and conservation in recently reclaimed areas have the potential to reduce this vulnerability by 45%. Hence, natural habitats have proved to be a great defense against coastal hazards and should be prioritized in coastal planning and development. The findings confirm that natural habitats are critical for coastal resilience and can act as a recovery force of coastal functionality loss. Therefore, we recommend that the Chinese government prioritizes restoration (where possible) and conservation of the remaining habitats for the sake of coastal resilience to prevent natural hazards from escalating into disasters.

Spatial–temporal rainfall assessments are integral to climate/hydrological modeling, agricultural studies, and water resource planning and management. Herein, we evaluate spatial–temporal rainfall trends and patterns in Pakistan for 1961–2020 using nationwide data from 82 rainfall stations. To assess optimal spatial distribution and rainfall characterization, twenty-seven interpolation techniques from geo-statistical and deterministic categories were systematically compared, revealing that the empirical Bayesian kriging regression prediction (EBKRP) technique was best. Hence, EBKRP was used to produce and utilize the first normal annual rainfall map of Pakistan for evaluating spatial rainfall patterns. While the largest under-prediction was estimated for Hunza (− 51%), the highest and lowest rainfalls were estimated for Malam Jaba in Khyber Pakhtunkhwa province (~ 1700 mm), and Nok-kundi in Balochistan province (~ 50 mm), respectively. A gradual south-to-north increase in rainfall was spatially evident with an areal average of 455 mm, while long-term temporal rainfall evaluation showed a statistically significant (p = 0.05) downward trend for Sindh province. Additionally, downward inter-decadal regime shifts were detected for the Punjab and Sindh provinces (90% confidence). These results are expected to help inform environmental planning in Pakistan; moreover, the rainfall data produced using the optimal method has further implications in several aforementioned fields.