Introduction

The moisture-holding capacity of the atmosphere increases with air temperature at a rate of ~7% °C−1, governed by the Clausius–Clapeyron (C-C) relation. This relationship should also hold for the intensity of extreme precipitation, unless there are large-scale circulation changes1,2. However, contrary to expectation, there is widespread observational evidence that short-duration (sub-daily or hourly) extreme precipitation increases with temperature beyond the C-C rate, the so-called super C-C scaling3,4,5,6,7,8. The rate may increase to almost double the C-C relationship at relatively high temperatures3,7. The strong increase in short-duration precipitation potentially leads to an increase in the magnitude and frequency of flash floods8. Hence, attention has focused on elucidating the mechanisms behind the super C-C scaling for short-duration precipitation extremes. To help interpret the mechanisms, one suggestion is to consider the type of precipitation9,10. Earlier studies have attempted to separate convective and large-scale precipitation based on observed cloud types7,11,12, lightning detection13, large-scale circulation patterns14, and event duration15. The consensus of these studies is that convective precipitation alone produces super C-C scaling in extreme events, emphasizing the primary contribution of local and short-lived precipitation events to super C-C scaling7,11,12,13,14,15.

However, synoptic-scale storms, typically associated with anomalous moisture flux or atmospheric rivers (ARs), also bring extreme precipitation, especially in mid-latitude regions16. Unlike local thunderstorms, synoptic-scale storms are characterized by persistent and large accumulated precipitation, which can cause widespread floods and landslides over East Asia during summer17 and over Western Europe and western North America during winter18,19. In the warm season, the interaction between summer monsoon and synoptic systems is a common feature in the extra-tropical regions such as East Asia and South America. In East Asia, such environment is favourable for long-persistent precipitation events, which often include some burst periods due to convective cells (or mesoscale convective systems) embedded in the stratiform precipitation area20. This type of storm recently led to devastating summer floods and landslides over East Asia, accompanied by the anomalous accumulated precipitation (e.g., in Japan in 2014 and 2018, and in central China and Japan in 202017,21,22). The scaling rate in this region provides evidence that the extreme values of longer-duration (daily) precipitation have unexpectedly larger scaling rates than shorter-duration precipitation (10 min and hourly)23. However, despite the significant socioeconomic impacts, the relationship between the scaling and synoptic situations for persistent precipitation events is still poorly understood8.

In the empirical scaling approach, daily analysis (i.e., fixed-interval statistics) can skew the results for long-duration events, because a single precipitation event can persist over a number of days, whereas daily precipitation may originate from a short-duration event, especially at higher temperatures10. In contrast, event statistics enable analysis of the life cycle and background weather patterns of individual storms7,15,24,25. A previous study also emphasized the importance of underlying storm characteristics (such as seasonal changes in dominant storm types and large-scale circulations) in the scaling analysis26. In East Asia, the two types of storms (i.e., AR-like synoptic storms and local convective storms) typically contribute to extreme precipitation during the warm season as mentioned above. As convective precipitation is more dominant at higher temperatures7,12, we would expect that the event statistics can distinguish convective precipitation arising from different storm types or synoptic patterns. Consequently, the classification of storm types in this region potentially leads to deeper insights into future change in convective storms and also different hydrological hazards such as flash floods and widespread floods.

Here we conducted C-C scaling analysis using an event-based precipitation dataset and compared scaling rates and synoptic weather patterns for both short- and long-duration events. In this study, short-duration events are defined as precipitation events whose duration are <5 h and the peak precipitation intensity occurs in the afternoon hours, whereas long-duration events are defined as those lasting longer than 10 h. We used temporally and spatially dense gauge precipitation data from Japan (Supplementary Fig. 1), comprising 646 stations with 10 min temporal resolution over a period of 25 years, with daily mean temperature. This enabled accurate measurement of extreme precipitation in this region, where various weather systems were related to extreme precipitation on different temporal scales27. The associated atmospheric conditions were also calculated from the Japanese 55-year Reanalysis (JRA-55). We only considered the warm season (May–September) to minimize the effect of seasonal changes in dominant weather patterns (i.e., southwesterly dominates in the warm season and northwesterly dominates in the cold season) on the scaling26. For each individual precipitation event, we calculated three event properties as follows: event duration, peak hourly (maximum) intensity, and total precipitation accumulation (Fig. 1).

Fig. 1: A schematic illustration for the identification of independent precipitation events.
figure 1

a Artificially produced time series of 10 min precipitation. Symbols D, R, and td indicate event duration, total precipitation accumulation, and dry (non-precipitation) duration, respectively. b Time series of 10 min precipitation (left axis, green bars) and accumulated hourly precipitation calculated every 10 min (right axis, circles) for Event #2. Symbols Ip and tp indicate event peak hourly precipitation and peak time, respectively. The scale of the x-axis has an interval of 10 min for both panels.

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Results

Synoptic patterns associated with extreme precipitation events

We find that the duration-based classification correctly distinguished precipitation statistics derived from different types of storms. Shorter-duration extreme events (typically <5 h) are more likely to occur around late afternoon, whereas longer-duration extreme events (>10 h) tend to dominate from midnight to early morning (Supplementary Fig. 2), suggesting that these two event types are triggered by different physical mechanisms. Figure 2 shows mean synoptic weather patterns for extreme precipitation events, defined as days where peak event intensity exceeded the 99th percentile at any station for each temperature bin (see ‘Methods’). It is obvious that long-duration extreme events were closely related to synoptic-scale large southwesterly moisture fluxes (Fig. 2a), which denoted ARs38, which globally provides 6 hourly atmospheric fields at 1.25° × 1.25° spatial resolution.

Event-based analysis

We created a database of precipitation events for each station using the 10 min precipitation series. Precipitation events have been detected by specifying a minimum inter-event time13,15,24,25. Following these studies, we defined an individual event as the period separated by a dry interval (i.e., continuous non-precipitation spell) of 3 h or longer. A sensitivity test was also performed using 1 and 6 h dry intervals, but these thresholds had no significant impacts on the scaling results. We considered precipitation events with a total amount >1.0 mm. Tropical cyclones also bring heavy precipitation in this region. However, extreme precipitation associated with tropical cyclones is also influenced by their intensity and such a dynamic effect could lead to a deviation from pure thermodynamic scaling39. Hence, we excluded days under the influence of tropical cyclones based on the best track data of the Japan Meteorological Agency, which were defined as days when a tropical cyclone was centred within a 1000 km radius from a station of interest. Applying this methodology, we detected 790,554 events in total over 25 warm seasons. For each event, we determined three event properties: event duration, peak hourly intensity, and event precipitation accumulation. The peak hourly intensity was defined as the maximum value of 60 min precipitation calculated for each 10 min interval within an event (see Fig. 1). Based on these event properties, 18%, 21%, 32%, and 29% of the events were associated with long-duration, mid-duration, afternoon-peak short-duration (defined as ‘short’ in this study), and morning-peak short-duration events, respectively (see below for each event definition).

In this study, we focused on two typical storms that cause extreme precipitation in this region: one was local thunderstorms and the other was synoptic-scale storms accompanied by anomalous moisture flux (e.g., ARs). Event duration was a critical factor to distinguish these storm types15,30. Supplementary Fig. 2 shows the diurnal variation in the relative frequency of peak hourly precipitation summarized for different event durations. Short-duration events (typically <5 h) were more likely to occur around late afternoon, which was more prominent for extreme hourly intensity. In contrast, longer-duration events (>10 h) tended to be dominant from midnight to early morning. Such a diurnal contrast between different event durations was also identified in previous studies40,41. The afternoon peak of short-duration events is probably due to the diurnal variation of surface solar heating, whereas for long-duration events the morning peak may be explained by the atmospheric condition before the maximum precipitation occurs (e.g., moisture accumulation and convection growth in the evening)41.

Based on these results, we classified precipitation events into two types based on their durations: short- and long-duration events. Short-duration events referred to precipitation events lasting <5 h with peak precipitation intensity in the afternoon hours (from 1200 to 0000 local time), based on the characteristics of local thunderstorms in the study region. On the other hand, long-duration events referred to those lasting longer than 10 h. To minimize the effects of different storm types on the scaling, mid-duration (i.e., 5–10 h) events and short-duration events with morning peak intensity were classified as neither short- nor long-duration events. However, as these events account for approximately half of all events, we also show the results of these excluded events similarly to the analysis for short- and long-duration events in Fig. 3 (Supplementary Fig. 6).

To analyse the synoptic-scale atmospheric conditions for extreme precipitation events, we composited JRA-55 reanalyses across all days above the 99th percentile of peak intensity at any station, i.e., extreme events occurring at multiple stations on the same day were considered as one event. We confirmed that the event duration-based classification distinguished two storm types in the large-scale atmospheric conditions, i.e., that short- and long-duration events corresponded to local thunderstorms and synoptic-scale storms, respectively (see Fig. 2 and main text for details).

Scaling of extreme precipitation

The relationship between extreme precipitation and temperature was investigated using a binning technique. Here we used an equal-distance method, following previous studies4,7,11. First, we assigned daily mean temperature on the day of each precipitation event to the corresponding event. If the event extended over multiple days, the average temperature of those days was used. The pairs of precipitation properties for each event (i.e., peak hourly intensity or precipitation accumulation) and daily mean temperature obtained from each station were pooled in one large data set, then placed in bins of 2 °C with steps of 1 °C (i.e., overlap** bins). In addition, to ensure a robust scaling relationship, the calculation was performed only within a temperature range from 11 °C to 27 °C, so that >80% of all stations were used for each bin (Supplementary Fig. 1b). For the binned data, we mainly computed the 99th percentiles of precipitation properties as a threshold for extremeness. To estimate the uncertainty, we also calculated the 95% confidence intervals of the percentiles using a bootstrap method. For a given temperature bin, 1000 samples of percentile values were estimated from randomly sub-sampled datasets of precipitation properties. Then 95% confidence intervals were determined based on the assumption that 1000 estimated values follow a normal distribution. The number of sub-samples was set to half the sample of the original data for a given temperature bin.

Finally, to identify the scaling of precipitation with temperature, we applied an exponential regression, which is widely used in scaling studies5,6,23. We fitted a least-squared linear regression to the logarithm of precipitation extremes as follows (Supplementary Fig. 7):

$$\log \left( P \right) = \beta _0 + \beta _1T$$
(1)

where P is precipitation and T is the corresponding temperature. The median temperatures of each bin were used to calculate the regression equation. Then scaling (ΔP% °C−1) was estimated using the exponential transformation of the regression coefficient β1:

$${\Delta}P = 100 \times ({\mathrm{e}}^{\beta _1} - 1)$$
(2)