Patterns of change in high frequency precipitation variability over north america

ABSTRACT Precipitation variability encompasses attributes associated with the sequencing and duration of events of the full range of magnitudes. However, climate change studies have largely

focused on extreme events. Using analyses of long-term weather station data, we show that high frequency events, such as fraction of wet days in a year and average duration of wet and dry

periods, are undergoing significant changes across North America. Further, these changes are more prevalent and larger than those associated with extremes. Such trends also exist for events

of a range of magnitudes. Existence of localized clusters with opposing trend to that of broader geographic variation illustrates the role of microclimate and other drivers of trends. Such

hitherto unknown patterns over the entire North American continent have the potential to significantly inform our characterization of the resilience and vulnerability of a broad range of

ecosystems and agricultural and socio-economic systems. They can also set new benchmarks for climate model assessments. SIMILAR CONTENT BEING VIEWED BY OTHERS POSITIVE CORRELATION BETWEEN

WET-DAY FREQUENCY AND INTENSITY LINKED TO UNIVERSAL PRECIPITATION DRIVERS Article Open access 01 May 2023 PRECIPITATION TRENDS DETERMINE FUTURE OCCURRENCES OF COMPOUND HOT–DRY EVENTS Article

Open access 14 March 2022 PATTERN CHANGE OF PRECIPITATION EXTREMES IN SVALBARD Article Open access 13 March 2025 INTRODUCTION Variability of high frequency precipitation, that is, the

variability associated with non-extreme events such as sequencing and persistence of daily precipitation, plays a significant role in a myriad of terrestrial functions. These include

ecosystem and agricultural productivity which are strongly tied to soil-moisture states, biogeochemical processes which are functions of moisture and temperature states, performance of

economic systems which depend on sustained availability of water, etc1. Although recent research has characterized the non-stationarity of extreme precipitation2,3,4,5,6,7 and its

intensification8,9,10 and the change in the mid-range variability such as seasonality11,12,13,14,15, little is known about trends of change in sequencing of frequent precipitation events

arising from climate change and other anthropogenic impacts. Understanding and accounting for changes in patterns of high frequency precipitation variability has the potential to inform

management and design of myriad systems dependent on hydrologic cycles and to improve predictability of creeping change and associated emergent patterns and risks16. For example,

agricultural yields and irrigation requirements are affected by changes in daily precipitation and its persistence17, 18. If precipitation magnitude is unchanged but precipitation events

last longer and are less intense and more frequent, the effect on agricultural management could be different than if precipitation fell in shorter but more intense and infrequent bouts.

Similarly, maintenance and continued efficiency of hydropower plants19 and other water resources-related systems would be affected by variations in moderate precipitation amounts20

potentially to the point of necessitating the revision of structural design standards or water management practices21, 22. Aquatic ecosystems are particularly vulnerable to changes in small

magnitude events, which can alter natural flow regimes23. Therefore, non-stationarity in precipitation sequencing, specifically as it pertains to high frequency precipitation, could prove to

be a crucial metric to use to strengthen global climate models’ long-term predictions24, 25 and provide much-needed information for a larger network of ecological, social, and economic

systems connected by the hydrologic cycle1. Unlike extreme precipitation, the cost associated with the repercussions from non-extreme precipitation trends is at present unknown. It may be

possible to ascertain a monetary cost of variability in non-extreme precipitation associated with dams and engineering systems, but profound effects on aquatic and terrestrial ecosystems

that may not be translatable to direct cost at present could prove to be expensive. Given the consequential impact of changes in patterns such as the fraction of rainy days in a year and

lengths of consecutive wet and dry periods, we investigate the presence and trends of change in such characteristics using long-term data from raingage measurements over North America. Some

studies of precipitation persistence conducted in Europe compare seasonal changes in wet and dry periods’ duration and occurrence with cyclone activity and temperature trends26 as well as

precipitation duration and intensity27, 28. One global study of temporal distribution of precipitation emphasizes the importance of light and moderate rainfall events29. Our study is the

first of its kind to study sequencing pattern changes specifically focusing on the effects on non-extreme precipitation across all of North America, although some precipitation studies have

focused on smaller sections of the continent30,31,32,33 or have focused on extreme precipitation in the United States34. Studying the variability at such a large scale enables us to

incorporate extensive raingage data (over 3,000 stations) and allows for spatio-temporal analysis on geographic scales ranging from continental to Level III Ecoregions35. Two studies, based

in the northeastern United States and central United States, studied the changes in distribution of intense precipitation by setting a minimum threshold of the station’s mean precipitation

value32 and at fixed values corresponding to precipitation ranges defining “moderately heavy”, “very heavy”, and “extreme” precipitation36. The northeast US study compared the persistence of

wet and dry periods of mean and extreme precipitation in order to compare with changes in total annual precipitation amounts32. The study concludes that non-stationarity in precipitation

trends is present, noting that both wet persistence and the 95_th_ percentile of daily precipitation are increasing. The central United States-based study compares changes in frequency of

intense precipitation to several climatological factors, such as tropical cyclone activity and mean annual temperature. Authors show that “very heavy” and “extreme” rain days have become

more frequent but that rain event characteristics such as duration and peak hourly rain intensity remain unchanged36. Another study based over all of North America analyzes changes in

duration of both warm seasons and the dry spells within them at a regional scale by defining minimum daily precipitation thresholds based on both precipitation amount and the corresponding

daily temperature37. This study demonstrates an increase in persistence of dry periods in eastern and southwestern regions of the United States in the past 40 years. Drawing on precipitation

data from several thousand stations, we choose to translate exceedance or non-exceedance of daily precipitation thresholds to a binary sequence, which allows us to study sequencing

patterns, precipitation persistence, and changing annual fractions of days above chosen precipitation thresholds. This method, therefore, includes the important effects of the full range of

magnitudes of precipitation and does not exclude the effects of non-extreme, or high frequency, precipitation in the analysis of long-term trends. We are then able to compare independent,

long-term trends for both fraction of wet days and persistence of wet and dry periods, and additionally, we can compare those to changes in daily rainfall magnitude. Through this comparison,

trends in changes of sequencing in high frequency precipitation events are examined across a large geographic scale. We explore the hypothesis that average daily precipitation, fraction of

days in a year receiving precipitation, and average length of consecutive wet and dry periods, may have independent trends at any station, and the clustering of stations with similar

behavior reveal spatially coherent trends. Our findings also lead us to investigate the potential role of regional climate or microclimate as an explanatory variable for changes in local

high frequency precipitation patterns. MATERIALS AND METHODS Daily precipitation data from the Global Historic Climatology Network (GHCN)38, one of the most complete global collections of

daily precipitation data39, for 7,194 stations in North America and Hawaii was made available through Earth Info, Inc. The data set was subjected to numerous, thorough quality control

procedures regarding both temperature and precipitation39, 40, and has been deemed appropriate for studies that analyze trends in light and heavy precipitation41, 42. Data was recorded with

a precision of up to a tenth of a millimeter (0.1 _mm_). In the GHCN data set, days with trace amounts of precipitation were flagged and assigned a zero value38. Due to the presence of

missing or unusable data, daily precipitation records for each station were filtered based on the criteria in Table 1. Stations meeting these requirements numbered 5,259. Additionally, a

station’s years of coverage were required to not end before the year 2000, which increases the chances stations will overlap in years of coverage so that fair temporal comparisons can be

made and with the hope that trends leading up to present-day may aid in future predictions. It is also assumed that “younger” stations would be better maintained and more numerous in

general. This additional requirement reduced the number of stations passing the quality control criteria (Table 1) to 3,030. Of the stations that did not pass filtering, 21% were eliminated

outright because the total number of available years (usable or not) was less than the minimum requirement of five decades. Several stations fell into clusters in remote locations, such as

the plains areas in southern Saskatchewan, Canada, which suggests the possibility that lack of ease of accessibility contributed to instrument errors or calibration issues, causing breaks in

data recording. In addition, stations in Canada have undergone changes in observational practices independent of those in the United States and Mexico, and such station inhomogeneity could

introduce bias in data records. However, the number of stations that pass the filtering tests in Canada are only a small fraction of the total number of stations analyzed. The influence of

other possible sources of inhomogeneity in station data across all of North America, such as changes in time of observation, were thoroughly considered and do not diminish the robustness of

this study. The locations of passing and failing stations are shown in Fig. 1, where filled circles indicate stations whose data passed all analysis requirements, and empty circles indicate

failing stations. After filtering, the years of coverage for passing stations fell between 1880 and 2010. The Fig. also indicates the number of stations with different lengths of coverage.

RESULTS AND DISCUSSION TEMPORAL AND SPATIAL PATTERNS OF CHANGE Data from the 3,030 stations that pass this quality control are used to compute the average daily precipitation, _ADP_(_y_),

for each year _y_ of acceptable data record based on all days in which a measurable amount of rainfall is recorded. Therefore, _ADP_(_y_) is can be equivalently converted to annual total

precipitation. Further, the daily precipitation data are converted to a binary sequence based on each day’s exceedance of a daily minimum threshold, _δ_. For each station, the binary

sequence _α_ _δ_ (_i_) is defined as: $${\alpha }_{\delta }(i)=\{\begin{array}{c}1\,{\rm{if}}\,R(i) > \delta \\ 0\,{\rm{otherwise}},\end{array}$$ (1) where _R_(_i_) is the recorded

precipitation total on the _i_ th day. We define _α_ _δ_ (_i_) as the precipitation/no-precipitation sequence based on each day’s exceedance or non-exceedance of 0.3 _mm_. The lowest

recorded daily rainfall amount across all stations was 0.3 _mm_. Therefore, this was chosen as the lowest threshold. However, we also consider higher thresholds associated with the 50th,

75th, 90th and 95th percentile of nonzero recorded precipitation for each station over its entire period of record (illustrated in Figure S1). These binary sequences of precipitation

disregard magnitude but capture the sequencing and persistence of events above a threshold _δ_. By choosing a range of _δ_ values, this method enable us to analyze the trends therein, if one

exists. Furthermore, from {_α_ _δ_ (_i_)} _i_ , for each year _y_ we compute the fraction of wet days (_P_ _R_ (_y_)), and average length of consecutive wet (_L_ _r_ (_y_)) and dry period

(_L_ _d_ (_y_)). The Mann-Kendall test at 5% significance level is used to determine the existence of statistically significant monotonically increasing or decreasing long-term trends for a

variable43. This test, which is consistent with other studies29, 32 is chosen because of its ability to handle gaps in data records44, which can be an issue with other statistical tests such

as the Sen Slope Method45. Slopes (Δ) for trends in each attribute _ADP(y)_, _P_ _R_ (_y_), _L_ _r_ (_y_), and _L_ _d_ (_y_) are determined by linear regression over the entire study period

for stations showing statistically significant trends (for illustration, see Figure S2). This approach provides a first order analysis, and there is a possibility that higher order trends

or short-term periodic behavior overlain on long-term trends are present at some stations. However, by testing for a monotonically increasing or decreasing behavior over a sufficiently long

study period, the overall, long-term trend can be captured. The breakdown of the number of stations showing trends in different attributes is illustrated in Fig. 2. We see that many stations

show a statistically significant trend in only one of the attributes and several stations show trends in a combination of these attributes. Finally, 516 stations show trends in all the

attributes. These attributes represent different facets of precipitation variability, and the results show that they can act independent of one another, meaning that presence of a trend in

one does not mandate that a similar behavior is present in another. Figure S3 in Supplemental Materials compares the distribution of analyzed statistics for all stations across all years of

data for both stations passing quality control for trend analysis and for stations in which a statistically significant trend is found. In addition to testing for statistically significant

trends in _P_ _R_ (_y_), _L_ _r_ (_y_), and _L_ _d_ (_y_), we tested for the presence of trends in average daily precipitation in a year, _ADP(y)_. In total, 1,182 stations showed a

statistically significant trend in _ADP(y)_. The majority of stations that are located in the American Northeast/Canadian Southeast and in the middle of the continent around the Mississippi

River indicate positive slopes in _ADP(y)_ (Fig. 3). Stations showing decreasing _ADP(y)_ were mostly limited to the American Southeast, the Pacific Northwest, and several pockets in the

American Southwest and at northern latitudes. These continent-scale patterns are quite similar to those shown previously46, although the previous results were based on decadal averages

compared to our study’s annual averages. The distribution of slopes, associated with linear trends of change, in _ADP(y)_ is presented in Table 2 as Δ_ADP(y)_ and in Fig. 6. The median of

slope, Δ_ADP(y)_, across all stations with a statistically significant trend is 5.34 × 10−3  _mm_/_day_/_yr_, which translates to an additional 97.5 _mm_ of precipitation in a given year

compared to 50 years prior. From Table 2 (graphically represented in Fig. 4), we also see that across North America, the median Δ_P_ _R_ (_y_), i.e. the median slope of _P_ _R_ (_y_),

indicates that the chance of any given day receiving precipitation in exceedance of 0.3 _mm_ is increasing by 5.98 × 10−4 per year. This means that in 2009, there were an extra 11 days with

recorded precipitation compared to 50 years earlier when the median number of days with precipitation was 96 days per year. The median value of Δ_L_ _r_ (_y_), i.e. the median slope of the

average number of consecutive days with precipitation, 2.77 × 10−3, indicates increasing persistence of precipitation events. The median value for Δ_L_ _d_ _(y)_, i.e. the median slope of

average number of consecutive days without precipitation, is −1.13 × 10−2. These results, which include only the stations that show statistically significant trend in the corresponding

variable, suggest that for a typical station among those showing a trend in 2009 saw wet periods 0.14 days longer than were seen 50 years earlier (when the median length of consecutive wet

days was 1.78 days), and consecutive dry periods were typically half a day shorter than a 50 years earlier (when the median length of consecutive dry days was 4.8 days). Although this

combination of trends is expected to be more prevalent, it is not necessarily the rule. It is possible that an area may see more days of precipitation (Δ_P_ _R_ (_y_) > 0), fewer

consecutive days of precipitation (Δ_L_ _r_ (_y_) < 0), and shorter periods without rain (Δ_L_ _d_ _(y_) < 0). This could mean that the area is receiving the same amount of

precipitation at smaller, more staggered intervals throughout the year or season, or it could mean that there are more frequent, more intense bouts of rain spread throughout the year

depending on Δ_ADP(y)_. There was not a significant difference in trends seen between the time periods with different start years (Figure S4). However, in order to ensure that stations with

different periods of temporal coverage did not introduce a bias, the above analysis was repeated for the same data subjected to the additional requirement that all usable data fall between

1960 and 2009. The resulting median values for Δ_ADP(y)_, Δ_P_ _R_ (_y_), Δ_L_ _r_ (_y_), and Δ_L_ _d_ (_y_) were 4.30 × 10−3, 5.03 × 10−4, 2.70 × 10−3, and −3.10 × 10−3, respectively.

Because these trends calculated in data from the same temporal periods were consistent with those found using all available usable data spanning different time periods, the latter was chosen

to establish long-term trends and ensure that all available data was used to inform on the analysis and inference. This additional testing also allowed for the comparison of trends in the

same stations but with different start times (e.g. trends in stations with data coverage from 1920 to 2009 could be compared to trends in those same stations from 1960 to 2009). Again,

results from this comparison showed consistency in trends, which lends itself to dispelling the possibility that outliers at the beginning of data sequences influence linear regression

results. Figure 5 illustrates the spatial variation of these temporal trends. Nonuniform spatial variation of sequencing patterns is apparent. The Northeastern United States and Pacific

Northwest show higher magnitude increase in slopes of both _P_ _R_ (_y_) and _L_ _r_ (_y_). It is observed that higher latitudes across the continent appear to show larger magnitude

increases in _P_ _R_ (_y_). Pockets in the northern Appalachian mountains, Gulf Coast states, and part of Hawaii also show strongly positive trends in _P_ _R_ (_y_). The middle of the

continent shows increasing slope in both _P_ _R_ (_y_) and _L_ _r_ (_y_), but the magnitudes are relatively smaller. North-central regions of the continent and parts of the American

Southeast also appear to show small-magnitude decreases in the slope of _P_ _R_ (_y_) and _L_ _r_ (_y_). However, in several areas dominated by positive Δ_P_ _R_ (_y_), such as the Pacific

Northwest and the Northeastern United States, stations showing negative Δ_P_ _R_ (_y_) in large magnitudes are also present. In contrast to the spatial patterns seen in Fig. 5, Fig. 6 shows

that trends for change in _L_ _d_ (_y_) are more concentrated to the Interior Plains, parts of the Rockies, and the Southwest. Coastal regions show smaller magnitude trends in _L_ _d_ (_y_)

compared to _L_ _r_ (_y_). Studying precipitation sequencing adds a new dimension to our understanding of changing precipitation patterns, especially in comparisons between precipitation

timing and, for example, trends in magnitude. Evidence of this is found in a notable difference between Fig. 3 and Fig. 5. Figure 3 illustrates that the middle of the continent, specifically

in the area of Texas, Oklahoma, and Missouri, indicates strongly positive Δ_ADP(y)_ over time. However, Fig. 5 illustrates that stations in these regions show lower rates of change in both

_P_ _R_ (_y_) and _L_ _r_ (_y_). On the other hand, areas such as the American Northeast/Canadian Southeast overlap with largely positive trends in both _P_ _R_ (_y_), _L_ _r_ (_y_), and

_ADP(y)_. Given the overall positive trends in both Δ_P_ _R_ (_y_) and Δ_ADP(y)_, it may be tempting to generalize that stations with increasing numbers of rainy days per year (i.e., Δ_P_

_R_ (_y_) > 0) would see an increase in magnitude of daily precipitation (i.e., Δ_ADP(y)_ > 0), and similarly, that fewer days of precipitation coincide with less recorded daily

precipitation. The former is not a poor generalization given that 84% of stations showing positive Δ_P_ _R_ (_y_) indicated increasing trends in daily precipitation amounts. However, of the

527 stations with negative Δ_P_ _R_ (_y_), 62% indicated increasing _ADP(y)_. In total, nearly a third of all stations showing a positive or negative trend in _P_ _R_ (_y_) do not fit the

generalization that more rainy days bring more rain or fewer rainy days bring less rain. This result challenges the generally accepted idea that with climate change ‘wet areas get more wet

and dry areas get drier’ and that rain events will become more intense10, and it is consistent with literature that has identified limitations of this assumption47. Depending on the

combination of trends in _P_ _R_ (_y_), _L_ _r_ (_y_), and _ADP_ for a given station, this generally accepted idea could prove to be an oversimplification. For example, positive Δ_P_ _R_

(_y_) and positive Δ_L_ _r_ (_y_) can coincide with negative Δ_ADP(y)_ as shown in Figure S2(d); this combination of trends connotes more dissipated, drawn out precipitation. This finding

demonstrates that long-term trends in precipitation patterns cannot be assumed based on knowledge of changes in magnitude or other precipitation metrics. Seasonal scale analysis of these

patterns (Figure S5) reveals intra-annual patterns that may not be visible in overall annual results. Although some regions, such as the American Northeast/Canadian Southeast, appear to

undergo fairly consistent changes year-round, the American Southwest, which does not demonstrate particularly clear trends in Fig. 5, shows more strongly seasonal trends, shown in Figure 

S5(b). In Spring and Autumn, this region shows mostly negative Δ_L_ _r_ (_y_) in combination with both increasing and decreasing _P_ _R_ (_y_). However, in Summer, which coincides with the

region’s monsoon season, the region shows an increasing fraction of days with precipitation and longer wet periods; this could have implications for monsoon patterns and many ecological

functions closely tied to pulsing precipitation patterns. Figure S5(b) demonstrates localized seasonal patterns, as well. The Central Valley region of California, which shows positive Δ_P_

_R_ (_y_) and Δ_L_ _r_ (_y_) year-round is surrounded by stations indicating negative Δ_L_ _r_ (_y_); this pattern is especially clear in Autumn. Figure 7 summarizes the trend statistics

when _δ_ is increased to the 50_th_, 75_th_, 90_th_, and 95_th_ percentile of precipitation distribution of each station. In addition to the significant decrease in the number of stations

showing nonstationarity in precipitation sequencing statistics, the range of Δ_P_ _R_ (_y_) for extreme precipitation also decreases as the threshold increases (Fig. 7). This means that not

only is the median Δ_P_ _R_ (_y_) for high frequency precipitation much higher than for low frequency precipitation amounts, but the largest magnitude slopes of high frequency precipitation

are much larger than those of low frequency precipitation. These results demonstrate that more stations are experiencing changes in non-extreme precipitation than extreme precipitation.

REGIONAL CLIMATE OR MICROCLIMATE IMPACTS Our study also shows the spatial clustering of many stations showing opposing trends in _P_ _R_ (_y_), _L_ _r_ (_y_), and _ADP(y)_. Figure 8

highlights five regions that are notable because they consistently show high levels of nonstationarity for both _P_ _R_ (_y_) and _L_ _r_ (_y_) and illustrate the presence of opposing trends

in closely located stations. This suggests the hypothesis that regional climate or microclimate variability is responsible for such opposing trends. The spatial anomalies shown in each

section in Fig. 8 are explored by analyzing ecoregion characteristics35 and changes in land cover as surrogates for microclimatic variations. Figure 8(a), which highlights the Pacific

Northwestern Coast of the United States, suggests that stations in Oregon showing strongly decreasing rates of _P_ _R_ (_y_) and _L_ _r_ (_y_) are mostly located in the Willamette Valley

(Level III Ecoregion 7.1.9, or _III-7.1.9_). In contrast, stations showing strongly positive Δ_P_ _R_ (_y_) and Δ_L_ _r_ (_y_) surround the Willamette Valley. The North Cascades ecoregion

(_III-6.2.5_) typically receives between 762 and 3810 _mm_ of rainfall per year48. The immediately adjacent Willamette Valley usually receives between 940 and 1500 _mm_ of rainfall per

year49. Daily precipitation amounts in Willamette Valley have been steadily declining (Figs 3 and S6), which is not the case in areas directly to the north and east. The Willamette Valley

differs in seasonal trends, as well. Most stations in the Pacific Northwest demonstrate strongly increasing _P_ _R_ (_y_) and _L_ _r_ (_y_) in every season. However, the Willamette Valley

shows that the ecoregion is experiencing decreases in _P_ _R_ (_y_) and _L_ _r_ (_y_) during Autumn and into Winter, times which nearly span rainy season (Figure S5(a)). Microclimates on a

much smaller scale could also be responsible for some long-term trends observed. The American Northeast and Canadian Southeast, shown in Fig. 8(d), consistently shows strongly increasing

trends in _P_ _R_ (_y_) and _L_ _r_ (_y_) year-round for all _δ_ thresholds except for several stations located in valleys and urban areas. Figure 3 indicates that nearly the entire region

is experiencing greater magnitudes of daily precipitation. In addition to the trends associated with microclimatic gradients across ecoregions, hot spots of opposing trends in precipitation

sequencing appear in areas that experience significant anthropogenic changes. The Ozark Highlands ecoregion (_III-8.4.5_ in Fig. 8(b)) shows decreasing _P_ _R_ (_y_) and _L_ _r_ (_y_), while

neighboring areas indicate the opposite trend. Decreasing trends in the Ozark Highlands are noted for all seasons but Autumn and were confirmed for extreme precipitation, as well. The

ecoregions located directly to the south, the Boston Mountains (_III-8.4.6_), Arkansas Valley (_III-8.4.7_), and the Ouachita Mountains (_III- 8.4.8_) show fairly strong increasing _P_ _R_

(_y_) and _L_ _r_ (_y_) trends. Although the Ozark Highlands do receive slightly less average annual precipitation and significantly less annual precipitation in dry years than the

surrounding ecoregions50, the ecoregion has experienced notably higher rates of growth both in terms of urbanization and transition from natural ecosystem to agriculture50. A similar

situation is observed in the Southern Coastal Plains (_III-8.5.3_) and the Southeastern Plains (_III-8.3.5_) ecoregions (Fig. 8(c)). Decreasing trends in _P_ _R_ (_y_) and _L_ _r_ (_y_) are

apparent throughout the Southern Coastal Plains ecoregion in Florida, Georgia, and South Carolina, as well as the southeastern foothills of the Appalachians for both non-extreme and extreme

(50_th_ and 75_th_ percentile thresholds) precipitation. Similar to the Ozark Highlands, the Southern Coastal Plains and Southeastern Plains ecoregions have experienced significant land

cover change, the highest and second-highest percentage change in the entire Southeastern United States, respectively51. Both ecoregions have experienced significant increases in population

over time, as well as frequent rotation between natural state, agricultural land, and forested land. The simultaneous influence on precipitation from both macroclimate and anthropogenic

factors is discussed in52 on a larger scale, but our results suggest that the influence of external forcings can be observed at a smaller, local microclimate scale. Although the Ozark

Highlands, Southern Coastal Plains, and Southeastern Plains ecoregions demonstrate similar sequencing trends, the regions differ greatly when compared in terms of magnitude of precipitation.

In Fig. 3, the Ozark Highlands area indicates increasing _ADP(y)_, while the southeastern United States shows strongly negative trends in _ADP(y)_. Similar opposing patterns can be seen in

Fig. 8(e), which highlights the ecoregions around the Great Lakes. In general, most stations in this area show Δ_P_ _R_ (_y_) > 0 and Δ_L_ _r_ (_y_) > 0 and also indicate Δ_ADP(y)_ 

> 0; the same is true for negative slopes. However, some stations showed significantly decreasing trends in _ADP(y)_ (see Fig. 3). These differences support the contention that

precipitation sequencing patterns capture variability that cannot be discerned directly from precipitation amounts. These observations suggest the possibility that different vegetative

covers and topographies could play a more prominent role than previously understood in long-term precipitation sequencing patterns and emphasize that large-scale regions cannot be

generalized in climate predictions given the evident strength of some local climates. Consideration of long-term sequencing trends on a local or regional scale suggests that long-term trends

in precipitation take on a more complex form, one that is likely determined by a combination of interconnected local climate and anthropogenic factors. The observations presented in this

analysis of more nuanced variability in trend highlight the importance of incorporating high frequency precipitation variability as a performance metrics for climate models. However, further

study is necessary to characterize the cause of these local trends. REFERENCES * Kumar, P. Hydrology: Seasonal rain changes. _Nature Climate Change_ 3, 783–784, doi:10.1038/nclimate1996

(2013). Article  ADS  Google Scholar  * Alexander, L. V. _et al_. Global observed changes in daily climate extremes of temperature and precipitation. _Journal of Geophysical Research:

Atmospheres_ 111, D05109, doi:10.1029/2005JD006290 (2006). ADS  Google Scholar  * Berg, P., Moseley, C. & Haerter, J. O. Strong increase in convective precipitation in response to higher

temperature. _Nature Geoscience_ 6, 181–185, doi:10.1038/ngeo1731 (2013). Article  CAS  ADS  Google Scholar  * Emori, S. & Brown, S. J. Dynamic and thermodynamic changes in mean and

extreme precipitation under changed climate. _Geophysical Research Letters_ 32, L17706, doi:10.1029/2005GL023272 (2005). Article  ADS  Google Scholar  * Giorgi, F. _et al_. Higher

hydroclimatic intensity with global warming. _Journal of Climate_ 24, 5309, doi:10.1175/2011JCLI3979.1 (2011). Article  ADS  Google Scholar  * Groisman, P. Y. _et al_. Trends in intense

precipitation in the climate record. _Journal of Climate_ 18, 1326–1350, doi:10.1175/JCLI3339.1 (2005). Article  ADS  Google Scholar  * Wentz, F. J., Ricciardulli, L., Hilburn, K. &

Mears, C. How much more rain will global warming bring? _Science_ 317, 233–235, doi:10.1126/science.1140746 (2007). Article  CAS  PubMed  ADS  Google Scholar  * Allan, R. P. & Soden, B.

J. Atmospheric warming and the amplification of precipitation extremes. _Science_ 321, 1481–1484, doi:10.1126/science.1160787 (2008). Article  CAS  PubMed  ADS  Google Scholar  * Min, S.-K.,

Zhang, X., Zwiers, F. W. & Hegerl, G. C. Human contribution to more-intense precipitation extremes. _Nature_ 470, 378–381, doi:10.1038/nature09763 (2011). Article  CAS  PubMed  ADS 

Google Scholar  * Trenberth, K. E. Changes in precipitation with climate change. _Climate Research_ 47, 123, doi:10.3354/cr00953 (2011). Article  ADS  Google Scholar  * Chou, C. _et al_.

Increase in the range between wet and dry season precipitation. _Nature Geoscience_ 6, 263–267, doi:10.1038/ngeo1744 (2013). Article  CAS  ADS  Google Scholar  * Dubois, N. _et al_.

Indonesian vegetation response to changes in rainfall seasonality over the past 25,000 years. _Nature Geoscience_ 7, 513–517, doi:10.1038/ngeo2182 (2014). Article  CAS  ADS  Google Scholar 

* Feng, X., Porporato, A. & Rodriguez-Iturbe, I. Changes in rainfall seasonality in the tropics. _Nature Climate Change_ 3, 811–815, doi:10.1038/nclimate1907 (2013). Article  ADS  Google

Scholar  * Holmgren, M., Hirota, M., van Nes, E. H. & Scheffer, M. Effects of interannual climate variability on tropical tree cover. _Nature Climate Change_ 3, 755–758,

doi:10.1038/nclimate1906 (2013). Article  ADS  Google Scholar  * Pascale, S., Lucarini, V., Feng, X., Porporato, A. & ul Hasson, S. Analysis of rainfall seasonality from observations and

climate models. _Climate Dynamics_ 44, 3281–3301, doi:10.1007/s00382-014-2278-2 (2015). Article  Google Scholar  * Wagener, T. _et al_. The future of hydrology: An evolving science for a

changing world. _Water Resources Research_ 46, W05301, doi:10.1029/2009WR008906 (2010). Article  ADS  Google Scholar  * Meng, Q. _et al_. Growing sensitivity of maize to water scarcity under

climate change. _Scientific reports_ 6, 19605, doi:10.1038/srep19605 (2016). Article  CAS  PubMed  PubMed Central  ADS  Google Scholar  * Riediger, J., Breckling, B., Svoboda, N. &

Schröder, W. Modelling regional variability of irrigation requirements due to climate change in northern germany. _Science of The Total Environment_ 541, 329–340,

doi:10.1016/j.scitotenv.2015.09.043 (2016). Article  CAS  PubMed  Google Scholar  * Kopytkovskiy, M., Geza, M. & McCray, J. Climate-change impacts on water resources and hydropower

potential in the upper colorado river basin. _Journal of Hydrology: Regional Studies_ 3, 473–493, doi:10.1016/j.ejrh.2015.02.014 (2015). Google Scholar  * Bates, B. C., Chandler, R. E.,

Charles, S. P. & Campbell, E. P. Assessment of apparent nonstationarity in time series of annual inflow, daily precipitation, and atmospheric circulation indices: A case study from

southwest western australia. _Water Resources Research_ 46, W00H02, doi:10.1029/2010WR009509 (2015). Google Scholar  * Shamir, E. _et al_. Climate change and water resources management in

the upper santa cruz river, arizona. _Journal of Hydrology_ 521, 18–33, doi:10.1016/j.jhydrol.2014.11.062 (2015). Article  ADS  Google Scholar  * Kunkel, K. E., Andsager, K. &

Easterling, D. R. Long-term trends in extreme precipitation events over the conterminous united states and canada. _Journal of Climate_ 12, 2515–2527, doi:10.1175/1520-0442 (1999). Article 

ADS  Google Scholar  * Bunn, E. S. & Arthington, H. A. Basic principles and ecological consequences of altered flow regimes for aquatic biodiversity. _Environmental Management_ 30,

492–507, doi:10.1007/s00267-002-2737-0 (2002). Article  PubMed  Google Scholar  * Good, S. P., Guan, K. & Caylor, K. K. Global patterns of the contributions of storm frequency,

intensity, and seasonality to interannual variability of precipitation. _Journal of Climate_ 29, 3–15, doi:10.1175/JCLI-D-14-00653.1 (2016). Article  ADS  Google Scholar  * Vaze, J. _et al_.

Climate non-stationarity–validity of calibrated rainfall–runoff models for use in climate change studies. _Journal of Hydrology_ 394, 447–457, doi:10.1016/j.jhydrol.2010.09.018 (2010).

Article  ADS  Google Scholar  * Zolina, O., Simmer, C., Gulev, S. K. & Kollet, S. Changing structure of european precipitation: Longer wet periods leading to more abundant rainfalls.

_Geophysical Research Letters_ 37, L06704, doi:10.1029/2010GL042468 (2010). Article  ADS  Google Scholar  * Zolina, O., Simmer, C., Belyaev, K., Gulev, S. K. & Koltermann, P. Changes in

the duration of european wet and dry spells during the last 60 years. _Journal of Climate_ 26, 2022–2047, doi:10.1175/JCLI-D-11-00498.1 (2013). Article  ADS  Google Scholar  * Zolina, O.

Multidecadal trends in the duration of wet spells and associated intensity of precipitation as revealed by a very dense observational german network. _Environmental Research Letters_ 9,

025003, doi:10.1088/1748-9326/9/2/025003 (2014). Article  ADS  Google Scholar  * Rajah, K. _et al_. Changes to the temporal distribution of daily precipitation. _Geophysical Research

Letters_ 41, 8887–8894, 2014GL062156, doi:10.1002/2014GL062156 (2014). * Trepanier, J. C., Roberts, M. J. & Keim, B. D. Trends and spatial variability in dry spells across the

south-central united states. _Journal of Applied Meteorology and Climatology_ 54, 2261–2272, doi:10.1175/JAMC-D-14-0319.1 (2015). Article  ADS  Google Scholar  * Groisman, P., Knight, R.

& Zolina, O. _Climate Vulnerability_, vol. 5, 22–55 (Elsevier, Amsterdam, The Netherlands, 2013). * Guilbert, J., Betts, A. K., Rizzo, D. M., Beckage, B. & Bomblies, A.

Characterization of increased persistence and intensity of precipitation in the northeastern united states. _Geophysical Research Letters_ 42, 1888–1893, 2015GL063124;

doi:10.1002/2015GL063124 (2015). * Pal, I., Anderson, B. T., Salvucci, G. D. & Gianotti, D. J. Shifting seasonality and increasing frequency of precipitation in wet and dry seasons

across the u.s. _Geophysical Research Letters_ 40, 4030–4035, doi:10.1002/grl.50760 (2013). Article  ADS  Google Scholar  * Kunkel, K. E., Easterling, D. R., Redmond, K. & Hubbard, K.

Temporal variations of extreme precipitation events in the united states: 1895–2000. _Geophysical Research Letters_ 30, 1900, doi:10.1029/2003GL018052 (2003). Article  ADS  Google Scholar  *

Wiken, E., Nava, F. J. & Griffith, G. North american terrestrial ecoregions-level iii. Tech. Rep., Commission for Environmental Cooperation (2011). * Groisman, P. Y., Knight, R. W.

& Karl, T. R. Changes in intense precipitation over the central united states. _Journal of Hydrometeorology_ 13, 47–66, doi:10.1175/JHM-D-11-039.1 (2012). Article  ADS  Google Scholar  *

Groisman, P. Y. & Knight, R. W. Prolonged dry episodes over the conterminous united states: New tendencies emerging during the last 40 years. _Journal of Climate_ 21, 1850–1862,

doi:10.1175/2007JCLI2013.1 (2008). Article  ADS  Google Scholar  * Menne, M. J. _et al_. Global historical climatology network - daily (ghcn-daily), version 3. NOAA National Climatic Data

Center Accessed: 01-24-2014 (2012). * Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E. & Houston, T. G. An overview of the global historical climatology network-daily database.

_Journal of Atmospheric and Oceanic Technology_ 29, 897–910, doi:10.1175/JTECH-D-11-00103.1 (2012). Article  ADS  Google Scholar  * Durre, I., Menne, M. J., Gleason, B. E., Houston, T. G.

& Vose, R. S. Comprehensive automated quality control of daily surface observations. _Journal of Applied Meteorology._ 49, 1615–1633, doi:10.1175/2010JAMC2375.1 (2010). Article  Google

Scholar  * Hoerling, M. _et al_. Characterizing Recent Trends in U.S. Heavy Precipitation. _Journal of Climate._ 29, 2313–2332, doi:10.1175/JCLI-D-15-0441.1 (2016). Article  ADS  Google

Scholar  * Wen, G., Huang, G. & Tao, W. Observed trends in light precipitation events over global land during 1961-2010. _Theoretical and Applied Climatology._ 125, 161,

doi:10.1007/s00704-015-1500-4 (2016). Article  ADS  Google Scholar  * Hirsch, R. M., Slack, J. R. & Smith, R. A. Techniques of trend analysis for monthly water quality data. _Water

Resources Research_ 18, 107–121, doi:10.1029/WR018i001p00107 (1982). Article  ADS  Google Scholar  * Esterby, S. R. Review of methods for the detection and estimation of trends with emphasis

on water quality applications. _Hydrological Processes_ 10, 127–149, doi:10.1002/(SICI)1099-1085 (1996). Article  ADS  Google Scholar  * Gilbert, R. _Statistical Methods for Environmental

Pollution Monitoring_, chap. 17, 225–240 (Van Nostran Reinhold Company Inc., New York, New York, 1987). * Our changing climate. Tech. Rep., National Climate Assessment, U.S. Global Change

Research Program, Washington, D.C. (2014). * Collins, M. _et al_. _Long-term climate change: projections, commitments and irreversibility_, chap. 12, 1029–1136 (Cambridge University Press,

2013). * Climate of Oregon. Western regional climate center 2016, Website, Accessed: 01-20-2016 (2016). * Oregon conservation strategy. Tech. Rep., Oregon Department of Fish and Wildlife,

Salem, Oregon (2006). * Ethridge, M. The ozark highlands. Tech. Rep. 2009–3065, US Geological Survey, US Department of the Interior, Columbia, Missouri (2009). * Drummond, M. A. Southern

coastal plain. Tech. Rep. 75, US Geological Survey, US Department of the Interior, Fort Collins, Colorado (2015). * Marvel, K. & Bonfils, C. Identifying external influences on global

precipitation. _Proceedings of the National Academy of Sciences_ 110, 19301–19306, doi:10.1073/pnas.1314382110 (2013). Article  CAS  ADS  Google Scholar  Download references ACKNOWLEDGEMENTS

This work was partially supported by Univ. of Illinois Graduate College Fellowship, a SURGE Fellowship, and NSF Grants CBET 1209402, EAR 1331906, and EAR 1417444. AUTHOR INFORMATION AUTHORS

AND AFFILIATIONS * Department of Civil and Environmental Engineering, University of Illinois at Urbana Champaign, Urbana, 61801, USA Susana Roque-Malo & Praveen Kumar * Department of

Atmospheric Sciences, University of Illinois at Urbana Champaign, Urbana, 61801, USA Praveen Kumar Authors * Susana Roque-Malo View author publications You can also search for this author

inPubMed Google Scholar * Praveen Kumar View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS P.K. and S.R. designed the study. S.R. conducted

the analysis. Both discussed the findings and contributed to the writing of the manuscript. CORRESPONDING AUTHOR Correspondence to Praveen Kumar. ETHICS DECLARATIONS COMPETING INTERESTS The

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