1. Introduction
Heavy precipitation and associated floods can have a major impact on society (Easterling et al. 2000; Rappaport 2000; Pall et al. 2011). Understanding mechanisms behind these extreme events is important for improving forecasting capabilities of the events and for estimating their frequency and intensity in a changing climate. The latter aspect is often addressed using fundamental thermodynamic arguments. According to these arguments, global-scale precipitation extremes are thought to intensify under global warming at a rate on the same order that atmospheric humidity increases, that is, faster than mean precipitation (Allen and Ingram 2002; Trenberth et al. 2003; Pall et al. 2007; Allan and Soden 2008). In addition to the atmospheric moisture content, variations in the lapse rate and vertical wind velocity are crucial for the exact magnitude of this intensification (O’Gorman and Schneider 2009a,b). On regional scales, changes in circulation regimes and moisture convergence are also important and can modulate the response of heavy precipitation to climate change (Meehl et al. 2005; Maraun et al. 2011). Both vertical motions and regional flow patterns are governed by atmospheric dynamics. For understanding and estimating regional changes of precipitation extremes, it is thus essential to take this link to the dynamical and meteorological forcing mechanisms into account.
The influence of large-scale circulation patterns such as ENSO on precipitation extremes is evident from a statistical perspective (Kenyon and Hegerl 2010; Yiou and Nogaj 2004) but difficult to understand mechanistically. In contrast, the life cycle of synoptic flow features like low pressure systems is directly linked to the formation of precipitation. For instance, extratropical cyclones induce a poleward moisture transport along sloping moist isentropes, leading to a lifting of air masses and the formation of clouds and precipitation (Browning 1986; Wernli and Davies 1997). Therefore, cyclones are typically associated with substantial surface precipitation (Field and Wood 2007) and can cause extreme precipitation and flooding, as shown in many case studies (e.g., Rappaport 2000; Kahana et al. 2002; Ulbrich et al. 2003, among others). However, the contribution of cyclones to precipitation extremes has not yet been quantified climatologically and on the global scale. Recent studies investigated concurrent variations in the occurrence of cyclones and (extreme) precipitation with changing climate using correlation analysis (Raible et al. 2007; Lionello and Giorgi 2007) or explored changes in simulated extremes of precipitation accumulated over cyclone tracks (Bengtsson et al. 2009; Champion et al. 2011). In this study, the relationship between cyclones and regional-scale precipitation extremes under present-day climate conditions is analyzed in an event-based manner, using single precipitation extremes with a spatial scale of roughly 10 000 km2 as a starting point. The question of how many of these extremes, defined as exceedance of the 99th percentile of precipitation at a given location, concur with the passage of a cyclone is addressed. In this way, it is possible to locally quantify the relevance of the causal relationship between cyclones and precipitation extremes.
In section 2a, the data used in this study will be described. In addition to the European Centre for Medium-Range Weather Forecasts Interim reanalysis (ERA-Interim), satellite-based precipitation estimates are applied as a reference for the precipitation extremes. Subsequently, section 2b will introduce the method that is used for relating precipitation extremes to cyclones, and section 2c will describe a corresponding significance test. Results of the comparison of ERA-Interim and satellite-based precipitation data as well as the average cyclone distribution will be presented in sections 3a and 3b, respectively. The main result of this study showing the relevance of cyclones for precipitation extremes will then be presented and discussed in section 3c. Section 3d will briefly analyze the characteristics of cyclones associated with extreme precipitation. Finally, conclusions and an outlook on future research will be given in section 4.
2. Data and methods
a. Reanalysis data and CMORPH precipitation estimates
For studying the link between cyclones and extreme precipitation, ERA-Interim data (Dee et al. 2011) from the European Centre for Medium-Range Weather Forecasts for the years 1989–2009 are employed. The advantage of using reanalysis data is that they are globally available with high, 6-hourly time resolution and that the circulation patterns (including cyclones) are well constrained by observations. Six-hourly global fields of sea level pressure (SLP) and prognostic precipitation are obtained from the ERA-Interim dataset. For precipitation, forecast steps from 6 to 12 and 12 to 18 h are used, neglecting the first six hours of a forecast because of potential model spinup. All data, which are originally available with a T255 spectral resolution, are interpolated to a 1° × 1° latitude–longitude grid. At each grid point, the 99th percentile of 6-hourly accumulated precipitation is calculated and all 6-h periods with precipitation larger than this percentile are identified as extreme events. This leads to 306 events per grid point, which is a compromise between statistical robustness (which would increase if a slightly lower percentile was used) and numerical feasibility (since all analyses are done on a gridpoint basis, a lot of data have to be processed). ERA-Interim precipitation values represent an average over model grid boxes, and the extremes thus differ in magnitude from those measured at stations (Dulière et al. 2011). Therefore, the focus of this study is on the investigation of extreme precipitation events that are not restricted to local scales, but occur in areas of at least 10 000 km2 (roughly the size of a 1° × 1° grid box) and potentially cause floods also in larger rivers.
Since ERA-Interim precipitation is obtained from short-term model forecasts, it is affected by forecast errors. It is not a priori clear if the ERA-Interim extreme events are sufficiently realistic to be used for further, process-oriented analyses. This is checked by comparing the reanalysis data with the satellite observation-based Climate Prediction Center morphing method (CMORPH) dataset (Joyce et al. 2004) for the overlapping period from 2003 to 2009. The CMORPH data comprise precipitation estimates with high spatial and temporal resolution, obtained from a combination of microwave and infrared satellite observations. The data are available for latitudes between 60°S and 60°N since December 2002. They have been validated against station observations from North America and Australia by Joyce et al. and showed an improved performance compared to other blending techniques based on similar satellite measurements. It should nevertheless be kept in mind that the representation of extreme precipitation events in this satellite-based dataset has not yet been systematically evaluated and that the associated uncertainties are thus largely unknown. Here, 3-hourly accumulated CMORPH precipitation estimates for the years 2003–09 on a 0.25° × 0.25° spatial grid are used. These data are further aggregated to 6-hourly time intervals and a 1° × 1° latitude–longitude grid for comparison with the reanalyses. As for the ERA-Interim data, extreme precipitation events are defined as exceedances of the local 99th percentile, leading to 102 events per grid point. Snow or ice at the surface cause missing values in the CMORPH precipitation estimates (see Joyce et al. 2004). All grid points are excluded from the analysis where more than 1% of the data are missing. To compare the two datasets, ERA-Interim precipitation extremes are recalculated using only data from 2003 to 2009 for the computation of the percentiles. Two extreme events in the ERA-Interim and CMORPH data are counted as simultaneous if the CMORPH event occurs with no more than a 6-h time difference from the ERA-Interim event in the same or one of the eight surrounding grid boxes. These small temporal and spatial offsets are allowed to minimize the influence of minor forecast errors on the comparison.
b. Cyclone identification and matching with precipitation extremes
From the ERA-Interim SLP fields, cyclones are detected with a slightly improved version of the algorithm introduced by Wernli and Schwierz (2006). This algorithm has the advantage that cyclones are identified as two-dimensional features with a certain spatial extent without any a priori assumptions on the cyclone radius. Local minima of SLP are detected, and a cyclone is defined as the area enclosed by the outermost closed SLP contour that contains one or several such minima. The SLP contours are identified at intervals of 0.5 hPa. The maximum length of the outermost closed contour is limited to 7500 km. Only SLP minima at grid points with altitude below 1500 m are used so as to neglect spurious SLP minima caused by extrapolation to sea level. In addition, cyclones are neglected for which the SLP difference between the minimum and the outermost contour is less than 1 hPa. For cyclone tracking, temporally successive SLP minima within a given search area are connected, taking into account the previous trajectory of the system. The dimensions of the search area are 700 km in the direction along the previous cyclone track segment, and 400 km in the perpendicular direction.
Precipitation extremes, accumulated over a 6-hourly time interval, are assumed to be affected by a cyclone if the respective grid point falls within a low pressure system either at the start or at the end of the 6-hourly period. In this way, only the local influence of the low pressure system is taken into account and, for example, potential effects of trailing cold fronts or spatially shifted, but associated troughs are not considered. The slight temporal smoothing of the cyclone fields induced by this comparison approach is also considered when calculating the relative cyclone frequency, which is obtained by dividing the number of 6-hourly periods affected by a low pressure system (either at the start or at the end of the period) Ncyc by the total number of 6-hourly intervals N in the analysis period. The percentage f of precipitation extremes coinciding with a cyclone is calculated as 
To investigate how the influence of cyclones on precipitation extremes depends on the time scale of the latter, precipitation time series are smoothed with a 1-day or 3-day running mean, centered around each 6-hourly interval. Extremes of these smoothed time series are then identified as described above (retaining the 6-hourly time granularity so as not to change the number of extreme events). At a given grid point, a precipitation extreme is associated with a cyclone if a low pressure system is present at least at one of the 6-hourly time instants in the respective 1-day or 3-day period. This by construction leads to larger cyclone frequencies and larger values of f compared to the original 6-hourly intervals. Furthermore, the seasonal cycle of the relevance of cyclones for extreme precipitation events is explored. Seasonal ERA-Interim precipitation extremes are identified by calculating the 99th percentile of 6-hourly accumulated precipitation separately for the two seasons from December to February (DJF) and June to August (JJA), leading to 75 events per grid box in DJF and 77 events in JJA. The seasonal percentage of events associated with a cyclone is then calculated as outlined above.
All analyses described up to here use the extreme precipitation events as a starting point. In contrast, when starting from the cyclones affecting a specific grid box, the question can be asked: how many of these cyclones cause a precipitation extreme at this location? For this type of investigation, cyclones have to be tracked in time, because one low pressure system may be present at a given location during several 6-hourly intervals. Note again that our identification algorithm considers cyclones as features with a finite size, determined by the outermost closed SLP contour. As a consequence, at any given time a particular low pressure system affects several grid points. All cyclone tracks touching a specific point at least once during their lifetime are associated with this point. Since this assignment of grid points and cyclone tracks is computationally elaborate, it is only done for every fifth point in latitude–longitude direction. The percentage 
c. Significance test
If cyclones and precipitation extremes are statistically unrelated, the extreme events can be regarded as a random sample of all 6-hourly time intervals in the period, meaning that 
To detect grid points where the relationship between cyclones and precipitation extremes is statistically not significant, a bootstrap test based on random allocations is performed. The occurrence of cyclones at selected base grid points is compared to randomly chosen precipitation events so as to obtain statistical distributions of matches between cyclones and precipitation extremes that occur purely by chance. Since the precipitation events should be physically unrelated to cyclones at the base locations, they are taken from grid points from the opposite hemisphere. Technically, the significance test works as follows: (i) 600 base grid points poleward of 20° latitude are randomly chosen, with average cyclone frequencies evenly distributed over the range from 0% to 60%. (ii) For every base grid point, 1000 event lists are constructed, consisting of 306 precipitation extremes each (306 is the number of extreme events identified at each point). Every event list is obtained by combining 51 successive extreme events from six points from the opposite hemisphere. This procedure is chosen so as to preserve the temporal autocorrelation properties of the precipitation extremes. (iii) For every base point the matches are counted between the occurrence of cyclones at the base point and the precipitation events from the constructed list. In this way, 600 statistical distributions of random matches between cyclones and precipitation extremes are obtained. In Fig. 1, the medians of these distributions are plotted against the cyclone frequency at the respective base grid points. They align nicely along the one-to-one line, indicating that f is similar to the relative cyclone frequency if cyclones and precipitation extremes are statistically unrelated, as noted above. The 1st and 99th percentiles of the distributions as a function of cyclone frequency are obtained from a quantile regression using the Frisch–Newton interior point method (Portnoy and Koenker 1997; Koenker 2011) and are plotted as dashed lines in Fig. 1. Finally, the relationship between cyclones and precipitation extremes at an arbitrary grid point is considered as statistically not highly significant if the percentage f of extreme events associated with a cyclone lies in between these dashed lines. For instance, at a grid point with a climatological cyclone frequency of 30%, matching percentages f between 21.6% and 38.4% are regarded as not significant. For the analysis of the temporally smoothed time series and the seasonal data, the same bootstrap approach is adopted.
Statistical distributions of matching frequencies of randomly constructed precipitation time series and cyclone time series at selected grid points. Medians (black crosses) and fitted 1st and 99th percentiles (dashed lines, obtained from a quantile regression) are plotted against the cyclone frequency at the respective grid points.
Citation: Journal of Climate 25, 19; 10.1175/JCLI-D-11-00705.1
3. Results and discussion
a. Comparison of precipitation extremes from different datasets
The magnitudes of the precipitation extremes from ERA-Interim and the CMORPH dataset, quantified in terms of the gridpoint-based 99th percentiles of 6-hourly accumulated precipitation, agree rather well in the extratropics poleward of approximately 30° latitude (see Fig. 2). However, in many regions in the tropics the magnitude of the ERA-Interim extremes is strongly underestimated compared to the CMORPH values. Nevertheless, for our study, which investigates the temporal coincidence with cyclones, it is not the magnitude but the timing of the precipitation events that is most crucial. To evaluate this timing, the relative fraction of precipitation extremes that occur simultaneously in both datasets is determined, allowing small temporal and spatial offsets (see section 2a). This relative fraction of simultaneous extreme precipitation events is shown in Fig. 3. The largest coincidence of events from the two datasets is found in the subtropics and midlatitudes, with mostly more than 70% and in certain areas more than 90% of simultaneous precipitation extremes. There are some regions, for example, over North America and at higher latitudes close to 60° where the coincidence is slightly reduced. Particularly in the more poleward areas, this might relate to reduced accuracy of the satellite data, for example, owing to the availability of data from the Tropical Rainfall Measuring Mission only equatorward of 38° (see again Joyce et al. 2004). Unfortunately, there are many missing values in several continental CMORPH precipitation time series in midlatitudes, prohibiting the comparison in these regions. In the tropics, where deep convection is the key process for intense precipitation, the frequency of simultaneous events is lower, around 50%, with minima over land below 20%. Such convective events are not explicitly simulated in the ECMWF model, but represented by a parameterization and thus less accurately captured than larger-scale precipitation in the extratropics. This seems to be particularly relevant over land areas. Overall this comparison shows that the timing of ERA-Interim precipitation extremes agrees well with satellite observations in the major part of the extratropics. The reanalysis dataset is thus well suited for analyzing the relationship between precipitation extremes and low pressure systems in these regions. Nevertheless, some uncertainty remains because ERA-Interim data cannot be evaluated poleward of 60° and over certain continental areas owing to the lack of CMORPH observations.
Geographical distribution of the 99th percentile of 6-hourly accumulated precipitation [mm (6 h) −1] for the period 2003–09: (a) 99th percentile from ERA-Interim data and (b) 99th percentile from CMORPH precipitation estimates. All data have been interpolated to a 1° × 1° latitude–longitude grid. Grid points with more than 1% missing values in the CMORPH dataset are masked in white.
Citation: Journal of Climate 25, 19; 10.1175/JCLI-D-11-00705.1
Coincidence of extreme precipitation events in reanalysis and satellite data. Relative fraction (%) of simultaneous 6-hourly precipitation extremes in ERA-Interim and CMORPH during 2003–09. Grid points with more than 1% missing values in the CMORPH dataset are masked in white.
Citation: Journal of Climate 25, 19; 10.1175/JCLI-D-11-00705.1
b. Cyclone frequency
Figure 4a shows the relative frequency of cyclones identified from the ERA-Interim SLP fields for the analysis period 1989–2009. This relative cyclone frequency has regional maxima of 40%–60% in the North Atlantic, North Pacific, and Southern Hemisphere storm track regions, as well as over land close to high mountain areas. The latter might be influenced by inaccuracies in the computation of sea level pressure over high topography (although this is minimized by only considering cyclone centers in areas where topography is below 1500 m). Furthermore, there are local maxima, for example, over Africa, probably related to the formation of heat lows. Most of these features are well known from previous climatologies (e.g., Hoskins and Hodges 2002; Wernli and Schwierz 2006), although here the absolute cyclone frequency values are relatively high since weak depressions are also taken into account and the data are smoothed over 6-hourly time intervals (see section 2b).
(a) Relative frequency (%) of cyclones in the ERA-Interim data (1989–2009). (b) Percentage f of 6-hourly precipitation extremes occurring simultaneously with a cyclone at the same grid point. Grid points are masked in white if the relationship between cyclones and extreme precipitation events is statistically not highly significant.
Citation: Journal of Climate 25, 19; 10.1175/JCLI-D-11-00705.1
c. Coincidence of precipitation extremes and cyclones
A cyclone is assumed to induce a precipitation extreme if both occur simultaneously at the same grid point. The percentage f of extreme precipitation events coinciding with a cyclone can then be quantified at every grid point (see section 2b). If at a certain location f is similar to the average cyclone frequency, the influence of cyclones on precipitation extremes is negligible. A statistical test based on bootstrapping is performed to identify grid points where this is the case (see section 2c). The geographical distribution of f is shown in Fig. 4b. In the storm track regions, values are often larger than 80% and thus strongly exceed the climatological cyclone frequency (cf. Fig. 4a). These cyclone-related precipitation extremes affect densely populated regions, in particular over the northeastern United States, the United Kingdom, northern Europe, and Japan, where f is on the order of 60%–80%. Also near the cyclone frequency maximum east of the Andes, there is a local maximum of f with values close to 70% near the Rio de la Plata estuary. Other regional maxima occur far away from the storm tracks and are associated with very low climatological cyclone frequencies. They are often located over the ocean, for example, west of Madagascar or off the northwestern Australian coast, but also extend to the adjacent land regions, in particular around the Mediterranean Sea, in eastern China, and over the Philippines and southeastern United States, where f reaches 50%–60% also over land. Several more equatorward maxima of f, for example, over the South China Sea, west of Central America, and northwest of Australia, are most probably associated with tropical cyclones (see also Lau et al. 2008; Knight and Davis 2009). The reduction of f over the continents is likely due to the increasing importance of other mechanisms for triggering precipitation extremes such as mesoscale convection or orographic lifting (Smith et al. 2003). The importance of the latter is evident from the local minima of f, for example, at the Norwegian coast, at the west coast of New Zealand, and in the European Alps where orography is steep. In agreement with this argument, there is a regional maximum of f over the west Siberian Plain, and lower values prevail in the surrounding mountainous regions. In the tropics there are large regions where there is no significant impact of cyclones on precipitation extremes. At these latitudes cyclones are generally rare and weak. The fact that these tropical regions often concur with areas where ERA-Interim does not capture well the timing of the precipitation extremes (cf. Fig. 3) indicates again that cyclone-induced precipitation extremes in the extratropics are better represented in the reanalysis compared to intense precipitation caused by tropical convection. Interestingly, over parts of Africa there is a significant negative relation between low pressure systems and precipitation extremes, suggesting that precipitation events do not coincide with strong continental heating (which favors the formation of heat lows). All together, Fig. 4b shows that the impact of cyclones on precipitation extremes is huge and highly significant in many regions all over the globe. It is not restricted to the main storm tracks, but rather is also of great importance in areas where cyclones are rare.
Qualitatively, the results presented in Fig. 4b do not depend on the time scale of the precipitation events. If ERA-Interim precipitation time series are temporally smoothed with a 1-day or 3-day running average, and all events for which a low pressure system occurs within this extended period are associated with this cyclone (see section 2b), both cyclone frequency and f increase, but there is still a significant difference between the two in many regions (not shown). The spatial patterns are very similar to the one shown in Fig. 4b for 6-hourly accumulated precipitation extremes. In midlatitudes and over the high-latitude oceans, almost all 3-day precipitation extremes coincide with a cyclone. Note, however, that for these 3-day events, the occurrence of the precipitation peak and the cyclone may not be simultaneous, making the causal relationship more uncertain.
The seasonal cycle of the percentage of precipitation extremes related to cyclones widely corresponds to the seasonality of the cyclone frequency (Fig. 5). It is stronger in the Northern than in the Southern Hemisphere storm track, in particular over the western Pacific. A pronounced seasonality of f is found in the Mediterranean area, where most cyclones and the associated precipitation extremes occur during boreal winter. Tropical cyclones and the corresponding extreme precipitation events predominantly occur in the respective summer season, leading to a strong seasonality of f over the North Atlantic west of Africa, the Philippine and South China Seas, parts of the tropical southwest Pacific and the Indian Ocean, as well as the North Pacific west of Central America.
Coincidence of cyclones and precipitation extremes in different seasons: relative cyclone frequency (%) in (a) DJF and (c) JJA; percentage of precipitation extremes related to a cyclone in (b) DJF and (d) JJA. Grid points are masked in white if the relationship between cyclones and extreme precipitation events is statistically not highly significant. Note that the smaller number of events leads to slightly more noisy spatial structures compared to Fig. 4b.
Citation: Journal of Climate 25, 19; 10.1175/JCLI-D-11-00705.1
d. Properties of cyclones causing extreme precipitation
In this section, not the precipitation events but the cyclone tracks affecting a specific grid point are used as a starting point for the statistical analysis. Two categories of such cyclone tracks can be distinguished: those that are associated with an extreme precipitation event at this location and those that are not. The percentage 
Percentage 
Citation: Journal of Climate 25, 19; 10.1175/JCLI-D-11-00705.1
Differences between (a) minimum core pressure and (b) latitudinal displacement relative to the genesis point (defined as the latitude of cyclogenesis minus the latitude of the extreme event) of cyclones causing an extreme precipitation event at the respective location and all other cyclones, shown for every fifth grid point in latitude–longitude directions. All fields are given in units of the local whole sample standard deviation of the respective quantity. Data are not shown where the relationship between cyclones and extreme precipitation events is statistically not highly significant (cf. Fig. 4b). Note that to improve visibility, plotting symbols are larger than the respective 1° × 1° grid boxes.
Citation: Journal of Climate 25, 19; 10.1175/JCLI-D-11-00705.1
4. Conclusions
In this study, ERA-Interim data have been used for quantifying the relevance of cyclones for extreme precipitation events. A comparison with CMORPH satellite estimates has shown that the ERA-Interim precipitation extremes, though their magnitude is often too small, properly represent the timing of the extreme events in major parts of the globe. They are thus well suited for investigating possible interrelations with the occurrence of cyclones. In many regions, not restricted to the main storm tracks, cyclones have been shown to be associated with a huge percentage of the precipitation extremes. This percentage might be even higher if other than the local influence of cyclones would be taken into account. In future research, remote effects of cyclones on extreme precipitation events, for example, via elongated frontal systems, may be considered additionally. Furthermore, the importance of tropical cyclones may be investigated more specifically and in more detail (see again Lau et al. 2008; Knight and Davis 2009). The results of this study show that the prediction of heavy precipitation and floods in weather forecasts often depends crucially on the accurate prediction of the associated cyclone track. Feature-based forecast methods (i.e., methods based on the identification of specific atmospheric flow features like cyclones) may thus be of great potential value (cf. Carley et al. 2011). Nevertheless, the relatively low fraction 
The feature-based perspective on the meteorological mechanisms of precipitation extremes obtained here is important for understanding and projecting changes in these events due to global warming, in particular on regional scales. Such changes can be due to the increasing atmospheric moisture content (Allen and Ingram 2002; Trenberth et al. 2003; Pall et al. 2007; Allan and Soden 2008), but can also be caused by variations in the dynamical forcing mechanisms. In many regions where a large percentage of precipitation extremes is related to cyclones, their future changes are specifically sensitive to variations in the abundance of these weather systems and the location of storm tracks.
Furthermore, it will be an essential next step to validate the spatial patterns of the coincidence of cyclones and precipitation extremes from present-day climate model simulations against the results obtained here for the reanalysis dataset. Such a process-based model validation (cf. Schaller et al. 2011) will provide important information about the proper representation of key mechanisms in the models for simulating precipitation extremes. Finally, the importance of cyclones for the occurrence of extreme precipitation events and the fact that it is nevertheless complicated to assess if and why a specific cyclone causes an extreme event motivate further process-based studies on precipitation formation in these weather systems (e.g., Wang et al. 2010; Field et al. 2011; Joos and Wernli 2012; Schäfler et al. 2011).
Acknowledgments
MeteoSwiss and ECMWF are acknowledged for giving access to ERA-Interim data, and the Climate Prediction Center at NOAA for providing CMORPH precipitation estimates. We thank M. Sprenger (ETH Zurich) for technical support with the cyclone identification algorithm as well as H. Joos, M. Böttcher, and H. C. Davies (ETH Zurich) for helpful comments. Comments by three anonymous reviewers have helped to improve the manuscript. The software package R (R Development Core Team 2010) has been used for producing the analyses and graphics for this study.
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