Rockfalls commonly exhibit power law volume‐frequency distributions, where fewer large events are observed relative to more numerous small events. Within most inventories, the smallest rockfalls are the most difficult to detect and so may not be adequately represented. A primary challenge occurs when neighboring events within a single monitoring interval are recorded as one, producing ambiguity in event location, timing, volume, and frequency. Identifying measurement intervals that minimize these uncertainties is therefore essential. To address this, we use an hourly data set comprising 8,987 3‐D point clouds of a cliff that experiences frequent rockfalls. Multiple rockfall inventories are derived from this data set using change detections for the same 10‐month period, but over different monitoring intervals. The power law describing the probability distribution of rockfall volumes is highly sensitive to monitoring interval. The exponent, β, is stable for intervals >12 hr but increases nonlinearly over progressively short timescales. This change is manifested as an increase in observed rockfall numbers, from 1.4 × 103 (30 day intervals) to 1.4 × 104 (1 hr intervals), and a threefold reduction in mean rockfall volume. When the monitoring interval exceeds 4 hr, the geometry of detected rockfalls becomes increasingly similar to that of blocks defined by rock mass structure. This behavior change reveals a time‐dependent component to rockfall occurrence, where smaller rockfalls (identifiable from more frequent monitoring) are more sensitive to progressive deformation of the rock mass. Acquiring complete inventories and attributing discrete controls over rockfall occurrence may therefore only be achievable with high‐frequency monitoring, dependent upon local lithology. Plain Language Summary: Rockfall inventories are required to model erosion, such as along coastlines or in mountain landscapes, and hazard from rockfall activity. The frequency distribution of rockfall volumes, commonly termed "magnitude‐frequency", is important for this modeling and for our understanding of how rockfalls occur and what drives them. For rockfalls and landslides in general, these distributions typically follow a power law, with relatively few larger rockfalls as compared to more numerous small events. Advances in hardware and algorithms have considerably improved the spatial resolution and precision with which a given rock face can be monitored using LiDAR. This has in turn improved our ability to detect small rockfalls, which in sum contribute significantly to overall volume loss from rock slopes in this setting. The improvement in spatial resolution has, however, considerably outpaced improvements in the temporal resolution of monitoring. If the interval between surveys is greater than the return interval of rockfalls, neighboring rockfalls within a single monitoring interval are recorded as one, producing ambiguity in event, timing, and volume. For the latter, this effect may amount to an order of magnitude variation. Our research aimed to examine the timescales over which rockfalls occur, allowing us to identify suitable monitoring intervals to discretize rockfalls. While conventional monitoring campaigns tend to acquire surveys at monthly intervals or longer, we draw upon a 1 hr resolution data set acquired over 10 months. We find that the interval of monitoring has a considerable impact on the probability distribution of measured rockfall volumes. An order of magnitude increase in rockfall numbers and a threefold decrease in mean rockfall volume are observed over timescales (monitoring intervals) of 1 hr, rather than 30 days. This is represented by a change in the power law exponent of the magnitude‐frequency relationship, which increases nonlinearly below timescales of ~12 hr. Interestingly, above ~12 hr, the exponent is stable, suggesting that changes in monitoring interval above this timescale will attain almost identical rockfall inventories. We explain this change in behavior by relating the geometry of rockfalls to the geometry of the blocks from which they are released. The average size of rockfalls identified over timescales below ~4 hr is comparable to the scale of individual discontinuities, indicating that fragmented detachments are more likely to control the increase in small events. As the timescale of rockfall monitoring increases, detachments become more similar to the rock mass structure, indicating structural control on failure. This behavior change suggests that smaller rockfalls are more sensitive to progressive deformation of the rock mass. This type of analysis is required to constrain the timescales over which this process occurs, which is necessary to understand prior to attributing specific drivers (such as storms) to rockfall occurrence. Key Points: The magnitude‐frequency distribution of rockfall inventories is highly sensitive to the time interval between monitoring surveysMonitoring intervals below ~12 hr yield a nonlinear increase in the number of rockfalls observed and a decrease in mean rockfall volumeAt monitoring intervals above ~12 hr, the distribution is stable and rockfall geometry converges with that defined by rock mass structure [ABSTRACT FROM AUTHOR]