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Understanding climate change impacts on Tropical Storm (TS) activity is crucial for effective adaptation planning and risk assessment, particularly in densely populated low‐lying delta rivers basins like the Ganges and Mekong. The change to TS characteristics with warming is uncertain due to limitations in global climate model resolution and process‐representation and storm tracking algorithms (trackers). Here, we used 13 HighResMIP models and two trackers to estimate the uncertainty in projections of TS characteristics. We found different trackers producing qualitatively similar but quantitatively different results. Our results show a decline (median ∼52%) in the frequency of TS but increase in the strongest TS and Available Cyclone Energy (ACE) of TS over both basins. The higher‐resolution models extract TS with much higher intensity and ACE values compared to the lower‐resolution models. These results have implications for adaptation planning and risk assessment for TS and suggest the need for further high‐resolution modeling studies. Plain Language Summary: Tropical Storms (TS) are one of the world's most damaging natural hazards which result in colossal socio‐economic losses to life, infrastructure, and property, especially in low‐lying delta rivers basins like the Ganges and Mekong. Knowledge of changes to TS activity under climate change can therefore be helpful in better disaster risk mitigation and climate adaptation. Previous modeling studies have used coarse‐resolution global climate models unable to capture key TS characteristics. In this study, we utilized finer resolution (up to ∼25 km at six hourly time‐steps) CMIP6 HighResMIP models and two different tracking algorithms (trackers) to resolve a part of this uncertainty. Our results project a decline to the frequency of future TS but an increase in the strength of TS (in terms of intensity and Available Cyclone Energy, qualitatively similar for both trackers). These findings can be used to assess the future resilience of existing infrastructure systems to Tropical Storms across these densely populated basins. Key Points: We used multiple CMIP6 HighResMIP models and two trackers to estimate the change in the Tropical Storms (TS) characteristics over the Ganges and Mekong basinsOur results show a decline in the future frequency of TS but increase in the future intensity and Available Cyclone Energy of TS over both basinsBoth tracking algorithms produce qualitatively similar but quantitatively different results [ABSTRACT FROM AUTHOR]

Finer grids in global climate models could lead to an improvement in the simulation of precipitation extremes. We assess the influence on model performance of increasing spatial resolution by evaluating pairs of high‐ and low‐resolution forced atmospheric simulations from six global climate models (generally the latest CMIP6 version) on a common 1° × 1° grid. The differences in tuning between the lower and higher resolution versions are as limited as possible, which allows the influence of higher resolution to be assessed exclusively. We focus on the 1985–2014 climatology of annual extremes of daily precipitation over global land, and models are compared to observations from different sources (i.e., in situ‐based and satellite‐based) to enable consideration of observational uncertainty. Finally, we address regional features of model performance based on four indices characterizing different aspects of precipitation extremes. Our analysis highlights good agreement between models that precipitation extremes are more intense at higher resolution. We find that the spread among observations is substantial and can be as large as intermodel differences, which makes the quantitative evaluation of model performance difficult. However, consistently across the four precipitation extremes indices that we investigate, models often show lower skill at higher resolution compared to their corresponding lower resolution version. Our findings suggest that increasing spatial resolution alone is not sufficient to obtain a systematic improvement in the simulation of precipitation extremes, and other improvements (e.g., physics and tuning) may be required. Key Points: Models generally agree on an intensification of precipitation extremes at higher spatial atmospheric resolutionObservational uncertainties are substantial for precipitation extremes, which makes the evaluation of the models difficultIncreasing spatial resolution alone is not sufficient to obtain a systematic improvement in the simulation of precipitation extremes [ABSTRACT FROM AUTHOR]