Changes in precipitation have far‐reaching consequences on human society and ecosystems as has been demonstrated by recent severe droughts in California and the Oklahoma region. Droughts are beside tropical cyclones the most costly weather and climate related extreme events in the U.S. We apply a weather type (WT) analysis to reanalysis data from 1979–2014 that characterize typical weather conditions over the contiguous United States. This enables us to assign precipitation trends within 1980–2010 to changes in WT frequencies and changes in precipitation intensities. We show that in the North Atlantic and Midwest region precipitation intensity changes are the major driver of increasing precipitation trends. In the U.S. Southwest, however, WT frequency changes lead to a significant precipitation decrease of up to −25% related to an increase in anticyclonic conditions in the North East Pacific. This trend is partly counteracted by increasing precipitation intensities.

1 Introduction The Northeast and Midwest regions of the contiguous United States (CONUS) show predominantly positive trends in observed annual mean precipitation since 1970, contrasting with decreases in parts of the U.S. Southwest (see Figure 1 for a definition of the regions) [McRoberts and Nielsen‐Gammon, 2011; Peterson et al., 2013; Balling and Goodrich, 2011; Williams et al., 2015]. The number of days with very heavy precipitation also increased significantly in the U.S. Northeast [Karl and Melillo, 2009], while the U.S. Southwest has experienced a spate of droughts [Cayan et al., 2010] leading to economic losses of more than 100 billion dollars since 2000 [Smith and Katz, 2013]. Historic droughts in the Southwest during the instrumental record have been mainly assigned to La Niña‐like sea surface temperature (SST) anomalies in the tropical pacific [Schubert et al., 2004a, 2004b; Seager et al., 2005; Herweijer et al., 2006; Huang et al., 2005; Dai, 2013], while short‐term droughts with durations of less than 3 months have been related to stationary Rossby wave patterns [Schubert et al., 2011]. Figure 1 Open in figure viewer PowerPoint Bukovsky, 2012 The CONUS is divided into five main regions (colors) and 17 climatological subregions []. Climate model simulations predict significant drying trends in the U.S. Southwest [Seager et al., 2007; Seager and Vecchi, 2010; Cook et al., 2015] and significant increases in precipitation in the U.S. Northeast and Midwest [e.g., Stocker et al., 2013]. The dominant processes causing the projected decreasing trends in the U.S. Southwest is a poleward extension of the subtropical dry zones leading to increasing anticyclonic conditions [Seager et al., 2007; Scheff and Frierson, 2012]. In the Northeast and Midwest the increasing water‐holding capacity of a warmer atmosphere plays a key role for increasing trends in addition to changes in large‐scale weather patterns [e.g., Trenberth, 2011]. There is some observational evidence that the increasing water‐holding capacity of the atmosphere leads to an increase of precipitation in the Northeast and Midwest [Karl and Melillo, 2009]. However, so far, observational evidence of a transition to a drier climate state in the Southwest arising from increasing anticyclonic conditions has been elusive due to the large natural climate variability [Seager and Vecchi, 2010]. In this study we use a weather classification algorithm applied to reanalysis data from 1979 to 2014, which allows us to separate precipitation trends due to changes in the frequency of weather types (WTs) from changes in precipitation intensity. Our data sources and methodology are presented in section 2. The weather type characteristics are described in section 3, and section 4 provides an assignment of trends.

2 Methods The Maurer [Maurer et al., 2002], Livneh [Livneh et al., 2013], Daymet [Thornton et al., 2014], and ensemble of the Newman [Newman et al., 2015] data sets are used for the precipitation analysis in section 4. These data sets provide daily precipitation for a common period from 1980 to 2010 and are conservatively (flux conserving) remapped to a 1/8∘ regular grid. The consideration of multiple observational data sets allows us to investigate the influence of observational uncertainties. The time period 1980 to 2010 is highly relevant because the bulk of the anthropogenic induced global warming occurred since around 1970 [Holland and Bruyère, 2014]. 2.1 Deriving Weather Types and Relating Them to Precipitation For the classification of WTs we us daily 12UTC sea level pressure (SLP), precipitable water (PW), and 700 hPa wind speed (UV700) data from ECMWF's Interim Reanalysis [Dee et al., 2011] (ERA‐Interim; 1979–2014). These variables are of importance to dynamic and thermodynamic processes leading to precipitation [Doswell et al., 1996; Lin et al., 2001] and are beneficial for the clustering of precipitation (see Figure S1 in the supporting information). The weather classification is performed with the cost733class‐1.2 software package [Huth et al., 2008]. Dominant WTs in CONUS are derived by applying the principal component analysis cluster analysis scheme (hereafter PCACA) that is a combination of two clustering algorithms [Comrie, 1996; Ekstrom et al., 2002]. First, a hierarchical cluster analysis [Romesburg, 2004] is applied to the dominant patterns of variability from a principal component analysis(all principal components contributing up to 90% variance are included). The algorithm treats every daily field as a separate cluster and merges the two nearest clusters on each hierarchical level of the process until the final number of clusters is reached. The second weather typing algorithm is a k‐means cluster analysis [Romesburg, 2004] that uses the outcome of the hierarchical clustering as starting partition. In each iteration of the clustering each daily field is placed in the cluster with the nearest cluster centroid (cluster average state). The Euclidean distance is used as the distance measurement. This iterative process keeps running as long as days are shifted between clusters. The PCACA scheme shows high skill in a WT intercomparison study [Schiemann and Frei, 2010]. We focus the classification on the CONUS within the boundaries 234.75° to 290.25°east and 30°to 48°north (see Figure 2 for the classification domain). Figure 2 Open in figure viewer PowerPoint SLP anomalies are shown in filled contours. The mean wind direction/speed at 700 hPa is shown as the direction/length of the arrows, while green contour lines show the mean PW values. The histograms show the relative monthly occurrence of the WTs during the year, and the gray dashed box shows the area that is used for the WT classification. The annual frequencies (FR) and the linear trend estimate (TR; including the significance level as a superscript if it is larger then 90%) of WTs are shown above each panel. Mean synoptic conditions and annual occurrence frequencies show distinct characteristics in the derived WTs. Before the application of the PCACA classification the SLP, PW, and UV700 fields are high pass filtered with a moving average Gaussian filter of 31 days length. The aim is to remove variability on time scales longer than those of synoptic weather systems (especially the annual cycle). This does not affect the spatial patterns of the input variables. The PCACA scheme requires a predefined number of WTs and a selection of atmospheric variables for the classification. To ensure well‐defined clusters we want to include days with similar precipitation patterns in each cluster (small intercluster variance), while we also want to have large differences in the precipitation patterns between the cluster (large intracluster variance) [Straus and Molteni, 2004]. In addition, we maximize the average absolute climatological precipitation anomalies of the WT centroids (mean precipitation field in WTs). Keeping the number of WT as low as possible leads to a final set of 12 weather types (supporting information Figure S1). 2.2 Trend Uncertainty Estimates We apply a simple ordinary least squares linear regression model [Wilks, 2011] for calculating trends. Trend estimates can be sensitive to the starting and end value of the investigated data series [Hannaford et al., 2013] especially if the time series has a high temporal variability. Therefore, we apply a resorting algorithm for deriving uncertainties of the trend estimates due to annual and decadal variabilities. The position of every data point (x i ,y i ) is thereby altered to (x j ,y j ) where i = 1,2,…31 (for 31 years of data) and j = i + X. X is a random variable with a normal distribution X ∼ N(−4,4). This means we assume that any data point could have been equally likely observed 4 years earlier or later. We truncate shifts so that the starting and end year cannot be exceeded, which affects points within 4 years from the starting and end. With this approach we generate 100 artificial data series and derive uncertainty estimates of the linear trend. 2.3 Trend Assignment ): (1) For the assignment of precipitation trends to WT frequency changes we assume that the precipitation intensities are time invariant and equal their climatological mean values (): pr d,n,m is the observed daily precipitation in the cell (n,m), and days (d = 1,…,N) within a certain WT. The annual precipitation related to the changes in WTs frequencies (PF a,n,m ) is the sum over multiplied by the annual frequency of the WTs (F WT,a ): (2) whereis the observed daily precipitation in the cell (), and days (= 1,…,) within a certain WT. The annual precipitation related to the changes in WTs frequencies () is the sum overmultiplied by the annual frequency of the WTs (): ), whereas the WT intensities can vary from year to year. In each year daily mean precipitation is calculated for each WT ( ): (3) Trend estimates assigned to WT intensity changes are based on the assumption that the WT frequencies are constant and equal to their climatological mean values (), whereas the WT intensities can vary from year to year. In each year daily mean precipitation is calculated for each WT (): d = 1,…,M are the days within a WT in the year a. The annual precipitation sums related to WT intensities (PI a,n,m ) are the sum over the WTs in multiplied by : (4) where= 1,…,are the days within a WT in the year. The annual precipitation sums related to WT intensities () are the sum over the WTs inmultiplied by 2.4 Subregions For a clear reference of locations we divide the CONUS into five main regions: Northwest, Southwest, Midwest, Northeast, and South. Each of these regions consist of three to five climatological homogeneous subregions (Figure 1) [Bukovsky, 2012]. Precipitation trend analyses are performed on the subregions. This has the advantage of averaging over a large number of time series (grid cells), which improves the robustness of the trend estimates by decreasing the influence of inhomogeneities in station densities, changes of measurement instruments, or changes in station location.

3 Weather Type Characteristics The WT centroids show distinctly differing pressure, flow, and moisture patterns (Figure 2). They have characteristic annual occurrence probability cycles with winter maxima in WT1–3 (Figures 2a–2c), transition season maxima in WT4–9 (Figures 2d–2i), and summer maxima in W10–12 (Figures 2j–2l). The annual frequencies of WT5–7 are significantly decreasing (according to the F test [Wilks, 2011]; −1%, −1.3%, and −1.3% per decade, respectively), while WT3 frequencies are significantly increasing (1.2% per decade). In addition, strong but insignificant decreases are found in WT9 (−1.1% per decade) and increases in WT12 (1% per decade). A common feature in the decreasing WTs is a low pressure anomaly over the eastern North Pacific (except for WT9). The decreasing frequency in low pressure anomalies is in line with significant positive trends in SLP over the North Pacific in the ERA‐Interim and Modern‐Era Retrospective Analysis for Research and Applications (MERRA) [Rienecker et al., 2011] reanalysis (except for June, July, and August; supporting information Figures S2 and S3). Both reanalysis data sets show partial increases in UV700 over the Northwest and a decrease in PW over the Southwest during winter and spring. The annual WT frequencies are not correlated with large‐scale climate indices such as the El Niño Southern Oscillation [Wolter, 1987] or the North Atlantic Oscillation Index [Hurrell, 1995] (supporting information Figure S4; correlation coefficients are typically below ±0.3 and insignificant). The highest correlation coefficients are found for detrended time series of the Arctic Oscillation Index [Thompson and Wallace, 1998] and WT3 (0.47) and WT4 (−0.68) frequencies. The correlation coefficients between the frequencies of WTs with significant decreasing trends (WT5–7) with ERA‐Interim SST are typically low (supporting information Figure S5) indicating that their decrease is not related to SST variability. There are distinct precipitation anomalies with strong gradients associated with the WTs (Figure 3). Some WTs have significant importance for the hydrology in specific subregions. For example, WT3 days that have an average frequency of 8%/yr contribute one quarter of the annual precipitation in the Pacific Northwest. Regions that are dependent on precipitation from a few WTs are especially vulnerable to changes in their frequency, e.g., the Pacific Southwest. The WTs that significantly decrease in frequency (WT5–7; Figures 2e–2g) are the source of 61% of the annual total precipitation in the Pacific Southwest. A decrease in their frequency can therefore have far‐reaching consequences on the hydrology and water resources in this region. Figure 3 Open in figure viewer PowerPoint Mean precipitation anomalies in the frequency normalized WTs (average over all days within the same WT) show distinct patterns and strong spatial gradients. Major mountain ranges, with an elevation difference larger than 450 m are shown in black contours. The mean anomalies from the four observational data sets are shown. Differences between the observational data sets are negligibly small.

4 Precipitation Trends Assignment The observed precipitation trends (Figure 4) show a distinct gradient with decreasing precipitation trends in the Southwest and Northwest and predominantly increasing trends in the other regions (except for the Southeast; Figure 4a). The strongest significant drying trends are found in the Central Southwest and the Southern Rockies, while the Pacific Southwest shows much lower and insignificant decreases (−2% per decade). The strongest significant increasing trends occur in the Atlantic Northwest. This is in line with findings in recent studies [McRoberts and Nielsen‐Gammon, 2011; Peterson et al., 2013; Balling and Goodrich, 2011]. Figure 4 Open in figure viewer PowerPoint (a) Observed annual subregion mean precipitation trends estimates and (b, c) their assignment to WT frequency and intensity changes. Subregions with significant trends are hatched (90% level). (d) Box‐whisker diagram for observed, WT frequency, and intensity trend estimates (black, red, and blue bars, respectively) in five subregions. Precipitation time series from four different observational data sets (red lines) and the extension of the Daymet data set to 2014 (orange line) ((e–g) black/blue lines show the mean/uncertainty of linear trends) for the Pacific Southwest. Precipitation changes arise from both WT frequency and intensity changes. The applied weather classification enables us to separate these two effects and to assign the observed precipitation trends to changes in the WT frequencies or changes in the WT intensity. The former is driven by changes in the large‐scale weather patterns, while the latter is influenced by smaller‐scale processes such as precipitation efficiency, atmospheric instability, or moisture availability. The trend related to changes in the frequency of WTs is estimated by assuming that precipitation intensities in the WTs do not change over time and are equal to the climatological mean. Trends related to changes in the intensity of precipitation within WTs are estimated by assuming that the WT frequencies are stationary and equal the climatological means. The trends related to changes in the WT frequency show a similar but more coherent and smoother pattern than observed precipitation trends with the strongest drying in the Pacific Southwest (−7% per decade) and decreasing trends inland (Figure 4b). All subregions in the Southwest and the Great Basin show significant drying trends. WT intensity changes (Figure 4c) show an insignificant 7% per decade increase in the Pacific Southwest which counteracts the significant WT frequency trend and results in a slightly negative trend. The main reason for the increasing trends in the WT intensity component is an intensification of daily precipitation in WT7 from 5 mm d−1 in 1980 to 7 mm d−1 in 2010. In contrast, the Central Southwest experiences a −8% per decade drying trend due to significant decreases in the WT intensities of WT12, WT5, and WT8 events. Adding this to the −3% per decade from WT frequency changes results in an observed trend of −11% per decade. In general, frequency and intensity trend components are of similar magnitude. Trend assignment on grid cells shows similar patterns to the estimates on subregions (supporting information Figure S6). Trends assigned to WT frequency changes have a 2 to 8 times lower uncertainty range (5th to 95th percentile difference) than trends due to WT intensity changes (Figure 4d). Thus, trends originating from WT frequency changes are much more deterministic, significant, and predictable than trends from WT intensity changes. For example, observed annual precipitation in the Pacific Southwest is highly variable (Figure 4e) with a maximum of 1118 mm a−1 in 1983 and a minimum of 167 mm a−1 in 2013. In comparison, the detrended WT frequency time series (Figure 4f) has a 10 times smaller variance and shows a significant drying trend. The WT intensity time series (Figure 4g) can be thought of as the observed precipitation without changes in WTs. It contains most of the natural variability, while the WT frequency time series is more related to the base state of the climate. For example, while 2013 was a record year for minimum precipitation with no changes in the WT frequency, the impacts would have been closer to the drought years of 1985 and 1989. WT frequency changes, however, reduced the precipitation in 2013 by an additional 25%. Repeating the trend assignment analysis with input data from the MERRA reanalysis gives similar results with slightly less pronounced drying trends due to WT frequency changes (supporting information Figure S7). Removing the trends from the ERA‐Interim data before the weather typing has found that trends in SLP amplify the drying due to WT frequency changes, whereas trends in PW and UV700 are of minor importance (supporting inforamtion Figure S8). The results are also robust against changes in the number of WTs (supporting information Figure S9).

5 Summary and Conclusions In summary, shifts in the frequency of WTs can have significant effects on the hydrology in the CONUS. WT frequency shifts are the dominant driver for decreasing precipitation in the U.S. Southwest and are responsible for minor increases of precipitation in regions along the Atlantic coast during the period 1980–2010. Our observational‐based results support projections of climate models that show a pronounced increase of droughts and aridity in the Southwest during the latter half of the 21st century due to a poleward extension of the subtropical dry zones leading to increasing anticyclonic conditions [Seager et al., 2007; Scheff and Frierson, 2012]. This is in line with our results since increasing SLP trends in the North Pacific are the main driver for WT frequency related drying trends in the Southwest (supporting inforamtion Figure S8). Also, the asymmetry in the projected drying in climate models [Scheff and Frierson, 2012] (with stronger decreases in spring) is visible in the WT frequency changes. The most relevant WTs for the hydrology in the Southwest (WT6 and WT7) have springtime maxima in their occurrence frequency and show significant decreasing trends. Our results also agree with studies that show a wetting of the North Atlantic and Midwest region mainly due an intensification of precipitation [Karl and Melillo, 2009; Stocker et al., 2013]. Along the Atlantic Coast WT frequency induced precipitation changes are positive but small (∼1% per decade). Projected changes of a poleward extension of the subtropical dry zones simulated by climate models and the corresponding decrease of precipitation in the U.S. Southwest have not been found in observations to date because of the large natural climate variability. Assigning precipitation trends to changes in WT frequencies reveals this process in observational data sets because it filters out the majority of variability in annual precipitation time series. This indicate that a transient drift of the Southwest's climate into a drier state has already started. This will be further investigated in a follow‐on study by focusing on WT frequency impacts on precipitation in climate model simulations.

Acknowledgments NCAR is funded by the National Science Foundation, and this work was partially supported by the Research Partnership to Secure Energy for America (RPSEA) and NSF EASM grant AGS‐1048829. We thank the ECMWF, NASA, NCAR, Santa Clara University, and University of Washington for making their observational data sets available.

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