<![CDATA[Abstract:Climate insurance is an increasingly essential adaptation instrument as the frequency and intensity of hydrometeorological hazards escalate under global climate change. The effectiveness of index-based climate insurance depends heavily on accurate risk assessments derived from atmospheric physics variables, climate prediction models, and satellite-based observational data. This study presents a Systematic Literature Review (SLR) of 30 primary studies indexed in Scopus, Web of Science, Springer, MDPI, and Sinta (2021–2025) to examine methodological advances in designing index-based climate insurance. The findings show that atmospheric parameters such as extreme rainfall, maximum temperature, drought indices (SPI/SPEI), vapour pressure deficit (VPD), soil moisture, and wind speed are the most reliable indicators for detecting flood, drought, heatwave, and storm risks. Climate and weather models (WRF, RCM, CMIP) demonstrate strong capability in representing climate patterns and extreme events, especially after downscaling and bias correction. Additionally, satellite products such as GPM, TRMM, MODIS, and SMAP play a crucial role in improving observational accuracy, particularly in data-scarce regions. The integration of physical parameters, climate prediction models, and high-resolution satellite data enables objective, measurable, and transparent threshold setting (trigger index) for index-based insurance design. Overall, this SLR confirms that atmospheric physics and advanced observational systems significantly enhance the precision of climate insurance schemes, supporting disaster risk mitigation, economic resilience, and evidence-based climate adaptation planning.]]>
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