<p>Rainfall erosivity (R-factor) represents the energy and intensity of rainfall events responsible for detaching and transporting soil particles, making it a critical component of widely used erosion prediction models such as the Universal Soil Loss Equation (USLE) and Revised Universal Soil Loss Equation (RUSLE). Despite its importance, accurate estimation of the R-factor is highly challenging, primarily due to the scarcity of high-temporal-resolution rainfall data, the reliance on region-specific empirical equations, and inconsistencies in methods for calculating rainfall intensity and kinetic energy. These limitations result in substantial uncertainties, particularly when assessing erosivity across varying climatic zones or under changing precipitation regimes influenced by climate change. This review aims to comprehensively evaluate the existing methods of rainfall erosivity assessment, examining their applicability and limitations across different climates. Special attention is given to the methodological diversity in deriving R-factor values from direct calculations using high-resolution rainfall data to proxy-based empirical models and how these approaches perform in capturing spatiotemporal rainfall variability. This study highlights that many conventional models tend to underestimate rainfall erosivity in regions characterized by short-duration, high-intensity storms, while overestimating it in areas with steady, low-intensity rainfall. Moreover, existing approaches often fail to adequately capture the impact of extreme rainfall events, which, though infrequent, contribute disproportionately to soil loss. These findings underscore critical gaps in current methodologies and raise concerns over their reliability for long-term erosion risk assessment and land management planning. To address these issues, we proposed a set of methodological improvements aimed at enhancing the accuracy, transferability, and climate-resilience of rainfall erosivity estimation. Strengthening the robustness of R-factor assessments will not only improve model predictions but also support more effective conservation strategies, particularly in the context of intensifying rainfall extremes driven by climate change.</p> Graphical Abstract <p></p>