Extreme rainfall in complex terrain is highly variable across space and time, challenging accurate estimation of design-relevant return levels. While rain gauge networks provide precise point measurements, their sparse distribution limits their ability to characterise this fine-scale variability and assess areal precipitation amounts. Weather radar offers the spatial coverage and temporal resolution required.</p> <p>In this study we leverage 9 years (2016–2024) of summer (June–August) precipitation data from the Swiss five radar, dual-polarisation C-band network (1 km, 5 min) to quantify summer precipitation extremes over Switzerland across durations from 30 min to 24 h and spatial aggregations from 1 to 500 km<sup>2</sup>. Return levels are estimated using the Simplified Metastatistical Extreme Value (SMEV) framework, which is well-suited to short, error-prone records, and validated against corresponding estimates from 60 long-term quality-controlled rain gauges. The resulting Intensity–Duration–Area–Frequency (IDAF) relationships explicitly capture how extremes vary jointly across space and time.</p> <p>Rainfall extremes exhibit a pronounced dependence on spatiotemporal scale, and vary spatially depending on the large-scale flows typical of the region and its topographic structure. For short durations and small areas, the largest return levels are concentrated in regions where strong orographic lifting is expected, including the Jura and north- and south-Alpine windward slopes, while lower values occur over the Plateau and inner Alpine valleys. As duration and area increase, small-scale peaks are progressively smoothed, and broader regions of high intensity return levels emerge, with the Southern Alps remaining a consistent hotspot across all scales. Analysis of three recent high-impact flood-producing storms illustrates how the spatiotemporal distribution of rainfall governs hydrological hazard, and how radar can capture localised extremes often missed by gauges.</p> <p>Overall, the resulting multiscale return level maps provide an improved basis for hydrological design and risk assessment in complex terrain, demonstrating the value of radar-based IDAF analysis and the ability of the framework to derive scale-aware flood-relevant extremes from short radar records.