Monthly Precipitation Time Series Research Articles

Hydrological drought class early warning using support vector machines and rough sets

Prediction of drought severity class/state can provide useful insight into preparedness actions. In this study, hydrological drought class, as determined by the standardized hydrological drought index (SHDI), was predicted. SHDI1 and SHDI3 drought classes were determined in seven and nine drought class systems. Support vector regression (SVR), support vector classification (SVC), and rough set theory (RST) were tested and compared as forecast modelling tools. Different combination of historic monthly streamflow and precipitation time series, or values/classes of SHDI and SPI, were considered as predictors to tackle the following question; is it possible to achieve the same or better accuracy by directly predicting the drought class, instead of predicting the streamflow (or drought index values) first and subsequently estimating the qualitative severity (class) of the drought? The results were more accurate in case of considering drought classes as inputs/output. However, the number of drought/wet classes may affect prediction accuracy. Prediction in case of fewer drought/wet classes leads to more accurate results. In addition, the SHDI3 prediction was more accurate than the SHDI1 prediction. RST showed slightly better accuracy than SVC and SVR. In case of nine-class forecast, the overall accuracy of RST model in correctly predicting the exact class, or with maximum one class shift, was 90.6% for SHDI1 and 96.5% for SHDI3. These values were 92.9% for SHDI1 and 98.8% for SHDI3 in seven-class system. Overall, direct classification methods (SVC and RST), in addition to simplicity, were more successful than the indirect method (SVR) in determination of severe dry/wet classes.

Homogenization and trends analysis of the Belgian historical precipitation time series

AbstractLong series of climate observations are essential to understand past and present climate change. However, the study of climate change from raw instrumental data poses a number of specific problems. Firstly, any observations are likely to contain erroneous values. Secondly, measurement conditions have generally changed over time which may introduce artificial breaks in the data that do not reflect the real variations of the climate. This article presents a new dataset of quality controlled and homogenized monthly precipitation series for Belgium. After in‐depth data quality control, monthly precipitation time series have been homogenized with the HOMER software on the basis of available metadata. Homogenization results are provided for 110 series over the period 1951–2019 and for 18 series starting before 1912 including nine covering the full time period 1880–2019. The new dataset of monthly homogenized series allows us to make a robust analysis of the precipitation evolution in Belgium from 1880 to nowadays. The comparison between the average of the last 30 years (1990–2019) with that of the first 30 years (1880–1909) of the considered time period, reveals an average increase in precipitation in the order of 15.5%. However, this average representation of the increase masks important seasonal and regional differences across Belgium.

Application of Signal Processing in Tracking Meteorological Drought in a Mountainous Region

This study addresses the application of signal processing in the evaluation of meteorological drought associated with monthly precipitation time series. Several drought indices and a Haar wavelet decomposition (WD) with ten components are implemented in the evaluation of the monthly precipitation of a mountainous region called Mount Uludag in Turkey. Monthly precipitation time series in three meteorological stations at the summit and foothills are used. The Standardized Precipitation Index (SPI) is used at monthly, annual, and 12- and 48-month moving average time frames as the benchmark to investigate the drought patterns. The results obtained by the WD and SPI are then confirmed using the Z-score index (ZSI) at monthly and annual scales, together with the modified China Z-index (MCZI) and rainfall anomaly index (RAI) at a monthly scale. Changes in the moments of the distribution, correlation analysis, mutual information, and power spectrum are applied to investigate the nature of the relationship between the sequences of precipitation events in time and space. The temporal correlation analysis, together with the mutual information, showed that the system has a short-term memory with strong seasonality. Similarly, the power spectra depicted major seasonality at 1, 3, 5, 6, 12, 22, and 60 months in the precipitation time series. It is concluded that the recent drought events have an infrequent nature, which altered the sinusoidal patterns of the large-scale events. The SPI-48 and the WD showed that declines are strongly related to the large-scale cycles, but the decline patterns are more related to the station located at the mountain summit.

The sensitivity of vegetation in the lower Tigris basin landscapes to regional and global climate variability

This study investigates the lower Tigris basin’s the normalized difference vegetation index (NDVI) sensitivity in 2000–2016 to regional climate variability reflected by the monthly precipitation and temperature time series of seven global datasets as well as to four global circulation indices. To examine the effect of climate variability on the different ecosystems, the study area has been classified into 10 smaller natural and anthropogenic landscapes based on landforms and land cover patterns. The preliminary analysis showed that the maximum biological productivity reflected by the NDVI of March and April has the highest correlation (0.5–0.8) to the same cumulative amounts of October–March period total precipitation and January–March period mean temperatures according to all datasets. In addition, this article showed there is a correlation between landscapes’ NDVI and global modulation represented by the September–February state of El Nińo-Southern Oscillation (ENSO) (0.55–0.70) and December state of the dipole mode index (DMI) (0.35–0.72). The significant differences in the original precipitation and temperature levels according to the different datasets have urged the use of normalized time series: z-score of temperatures and analogous six-months the standardized precipitation index (SPI). However, the multiple correlation analysis showed that using ERA-

Evaluation of spatial–temporal characteristics of precipitation using discrete maximal overlap wavelet transform and spatial clustering tools

Abstract In the present study, classical and proposed methods were used to investigate the monthly precipitation characteristics of 30 stations in the southeastern United States during 1968–2018. Maximal overlap discrete wavelet transform (MODWT) as preprocessing method and K-means clustering method were used. First, the monthly precipitation time series of stations were decomposed into several subseries using MODWT and considering db as the mother wavelet. Then, the energy values of theses subseries were calculated and used as inputs in K-means and radial basis functions (RBF) methods. The optimum number of clusters obtained for the considered stations in both classical and proposed methods was five clusters. In order to use the data as the input of the RBF method, the data correlation was evaluated by variogram. Based on the results of clustering and in accordance with the latitude and longitude variations of the stations, it was found that with increasing the energy of the clusters, the amount of precipitation in the stations decreased and vice versa. The silhouette coefficient of clustering for the classical method obtained was 0.3 and for the proposed method it was 0.8, which indicates better clustering of the selected area using the proposed method.

Performance evaluation of different probability distribution functions for computing Standardized Precipitation Index over diverse climates of Iran

AbstractMonthly precipitation time series of 46 synoptic stations distributed over diverse climates of Iran were investigated to identify the best‐fitted probability distribution function (PDF) to the monthly precipitation series aggregated at different timescales. The computed skewness and the probability of zero precipitation (PZP) values of the considered precipitation aggregations show that a large proportion of monthly precipitation data in the study period, especially in the warm months of the year, is zero. The skewness of all 12 individual monthly precipitation time series as well as their aggregation at 3‐ and 6‐months timescales were also found very high in many studied stations, particularly in those of the arid and semi‐arid climates of Iran. To identify the best candidate distribution function for computing Standardized Precipitation Index (SPI) in different climatic areas of Iran, the performances of a dozen statistical PDFs in fitting monthly precipitation aggregated at different timescales were statistically evaluated. The results showed that the two‐parameter gamma distribution best fits monthly precipitation time series over most parts of the country while the Pearson Type III (PE3) was identified as the best‐fitted model for 3‐ and 6‐months aggregation periods and general extreme value (GEV) distribution as the best candidate function in fitting precipitation aggregated at 9‐, 12‐, and 24‐month timescales. PE3 was also identified as the second best‐fitted model for both the shorter time scale (1‐month) and the longer timescales (9‐, 12‐, and 24 months). Considering that PE3 ranked either first or second in fitting precipitation time series aggregated at different timescales and in all climatic areas of Iran, it is chosen as the most appropriate distribution function in fitting precipitation aggregated at all considered timescales throughout Iran.

Dry and Wet Climate Periods over Eastern South America: Identification and Characterization through the SPEI Index

A large part of the population and the economic activities of South America are located in eastern regions of the continent, where extreme climate events are a recurrent phenomenon. This study identifies and characterizes the dry and wet climate periods at domain-scale occurring over the eastern South America (ESA) during 1980–2018 through the multi-scalar Standardized Precipitation–Evapotranspiration Index (SPEI). For this study, the spatial extent of ESA was defined according to a Lagrangian approach for moisture analysis. It consists of the major continental sink of the moisture transported from the South Atlantic Ocean throughout the year, comprising the Amazonia, central Brazil, and the southeastern continental areas. The SPEI for 1, 3, 6, and 12 months of accumulation was calculated using monthly precipitation and potential evapotranspiration time series averaged on ESA. The analysis of the climate periods followed two different approaches: classification of the monthly SPEI values as mild, moderate, severe, and extreme; the computation of the events and their respective parameters (duration, severity, intensity, and peak). The results indicate that wet periods prevailed in the 1990s and 2000s, while dry conditions predominated in the 2010s, when the longest and more severe dry events have been identified at the four scales.

The Development of a Hybrid Wavelet-ARIMA-LSTM Model for Precipitation Amounts and Drought Analysis

Investigation of quantitative predictions of precipitation amounts and forecasts of drought events are conducive to facilitating early drought warnings. However, there has been limited research into or modern statistical analyses of precipitation and drought over Northeast China, one of the most important grain production regions. Therefore, a case study at three meteorological sites which represent three different climate types was explored, and we used time series analysis of monthly precipitation and the grey theory methods for annual precipitation during 1967–2017. Wavelet transformation (WT), autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) methods were utilized to depict the time series, and a new hybrid model wavelet-ARIMA-LSTM (W-AL) of monthly precipitation time series was developed. In addition, GM (1, 1) and DGM (1, 1) of the China Z-Index (CZI) based on annual precipitation were introduced to forecast drought events, because grey system theory specializes in a small sample and results in poor information. The results revealed that (1) W-AL exhibited higher prediction accuracy in monthly precipitation forecasting than ARIMA and LSTM; (2) CZI values calculated through annual precipitation suggested that more slight drought events occurred in Changchun while moderate drought occurred more frequently in Linjiang and Qian Gorlos; (3) GM (1, 1) performed better than DGM (1, 1) in drought event forecasting.

Changes in the Characteristics of Dry and Wet Periods in Europe (1851–2015)

This study spanning the period 1851–2015 explores the spatial and temporal characteristics of dry and wet periods in Europe as well as their variability and changes. It is based on up to 220 stations with monthly precipitation time series that have a varying data availability within the study period. The stations are classified into eight regions with similar climate characteristics. Dry and wet periods are analyzed using the decile method as well as the modified Rainfall Anomaly Index mRAI at the 3-month timescale. Spatial extent, duration, and frequency of dry and wet periods show a large multi-decadal variability resulting in comparatively small long-term trends over the entirety of Europe for the study periods 1901–2015 and 1951–2015. Nonetheless, several sub-regions show distinct changes—with opposite signals for northern and southern Europe. Spatial extent and duration of dry periods generally decreased, while wet periods show increases throughout the 20th century—particularly in Scandinavia. A simultaneous increase in the frequency of severely dry and wet years, respectively, is observed since the 1980s. This indicates that temperature increases across Europe may be connected with an increasing frequency of extremes at both sides of the probability density function of precipitation.

High-resolution monthly precipitation and temperature time series from 2006 to 2100

Predicting future climatic conditions at high spatial resolution is essential for many applications and impact studies in science. Here, we present monthly time series data on precipitation, minimum- and maximum temperature for four downscaled global circulation models. We used model output statistics in combination with mechanistic downscaling (the CHELSA algorithm) to calculate mean monthly maximum and minimum temperatures, as well as monthly precipitation at ~5 km spatial resolution globally for the years 2006–2100. We validated the performance of the downscaling algorithm by comparing model output with the observed climate of the historical period 1950–1969.

Relationship between precipitation anomalies and multivariate ENSO index through wavelet coherence analysis

A better understanding of the local and regional spatio-temporal variability of past precipitation is needed to contextualize climate change research. Monthly precipitation data from 59 stations in the state of Zacatecas, Mexico were transformed into standardized monthly precipitation anomalies for the period 1976-2010 (34 years). Cluster analysis of the new time series was used to identify regions with similar precipitation regimes. Power spectrum analysis was carried out to identify important frequencies. Wavelet coherence analysis was performed to detect significant relations between each of the regional mean standardized anomalies of the monthly precipitation (SAMP) time series and the Multivariate El Nino Southern Oscillation Index (MEI). Three regions were identified: semi-arid, highlands and canyons. In all the three regional SAMP series, the 0.5-, 1-, 2- and 3-year frequencies are present in the power spectrum; the highlands and canyons regions showed important frequencies of 13 and 12 years, respectively. The 3-year frequencies may be linked to El Nino Southern Oscillation phenomenon. Three periods with significant correlation between regional SAMP time series and the MEI were identified: 1993 to 2003 for the semi-arid region, 1995 to 2003 for the highlands region and 1988 to 2003 for the canyons region. The results suggest that precipitation anomalies vary over time and according to regions. The results also strongly indicate that precipitation is modulated by the MEI.

Spatio-Temporal Trends of Monthly and Annual Precipitation in Aguascalientes, Mexico

The objective of this research was to analyze the temporal patterns of monthly and annual precipitation at 36 weather stations of Aguascalientes, Mexico. The precipitation trend was determined by the Mann–Kendall method and the rate of change with the Theil–Sen estimator. In total, 468 time series were analyzed, 432 out of them were monthly, and 36 were annual. Out of the total monthly precipitation time series, 42 series showed a statistically significant trend (p ≤ 0.05), from which 8/34 showed a statistically significant negative/positive trend. The statistically significant negative trends of monthly precipitation occurred in January, April, October, and December. These trends denoted more significant irrigation water use, higher water extractions from the aquifers in autumn–winter, more significant drought occurrence, low forest productivity, higher wildfire risk, and greater frost risk. The statistically significant positive trends occurred in May, June, July, August, and September; to a certain extent, these would contribute to the hydrology, agriculture, and ecosystem but also could provoke problems due to water excess. In some months, the annual precipitation variability and El Niño-Southern Oscillation (ENSO) were statistically correlated, so it could be established that in Aguascalientes, this phenomenon is one of the causes of the yearly precipitation variation. Out of the total annual precipitation time series, only nine series were statistically significant positive; eight out of them originated by the augments of monthly precipitation. Thirteen weather stations showed statistically significant trends in the total precipitation of the growing season (May, June, July, August, and September); these stations are located in regions of irrigated agriculture. The precipitation decrease in dry months can be mitigated using shorter cycle varieties with lower water consumption, irrigation methods with high efficiency, and repairing irrigation infrastructure. The precipitation increase in humid months can be used to store water and use it during the dry season, and its adverse effects can be palliated with the use of varieties resistant to root diseases and lodging. The results of this work will be beneficial in the management of agriculture, hydrology, and water resources of Aguascalientes and in neighboring arid regions affected by climate change.

Comparison and Bias-Correction of Satellite-Derived Precipitation Datasets at Local Level in Northern Kenya

Understanding ongoing trends at local level is fundamental in research on climate change. However, in the Global South it is hampered by a lack of data. The scarcity of land-based observed data can be overcome through satellite-derived datasets, although performance varies according to the region. The purpose of this study is to compute the normal monthly values of precipitation for the eight main inhabited areas of North Horr Sub-County, in northern Kenya. The official decadal precipitation dataset from the Kenyan Meteorological Department (KMD), the Global Precipitation Climatology Centre (GPCC) monthly dataset and the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) monthly dataset are compared with the historical observed data by means of the most common statistical indices. The GPCC showed the best fit for the study area. The Quantile Mapping correction is applied to combine the high resolution of the KMD dataset with the high performance of the GPCC set. A new and more reliable bias-corrected monthly precipitation time series for 1983–2014 results for each location. This dataset allows a detailed description of the precipitation distribution through the year, which can be applied in the climate change adaptation and tailored territorial planning.

A Spatiotemporal Classification of the Peruvian Precipitations Between 1990 and 2015

Precipitation and its variations have great importance in water resource management and sustainable development. In this study, the Peruvian precipitations between January 1990 to October 2015, were used. The precipitations were classified under spatial, temporal, and spatiotemporal classes. For this aim, properties of the precipitation time series including the monthly mean, monthly standard deviation, and principal components at monthly and annual scale were evaluated. Results were projected on a map using the Kriging method. Later, the double mass curves of the monthly precipitation time series were used to classify the temporal changes in the precipitations. Thereafter, the Spearman rank-order correlation was used to evaluate the spatiotemporal changes in monthly and annual precipitation time series by projected t-values on the Peruvian map. Finally, precipitations time series were plotted against Koppen–Geiger climate class of each station and several large scale oscillations namely North Atlantic Oscillation (NAO), El Nino/Southern Oscillation (ENSO), Atlantic Multi-decadal Oscillation (AMO), and Pacific Decadal Oscillation (PDO) simultaneously. It was concluded that there are at least three major climatic regions in the country. Spatial classes, depicts that the Andes Ranges is a major role player in the climate of the country while the ENSO and PDO are the main drivers of the precipitation extremes. Results also indicated to an ascending changes in the amount of precipitation from west to east, while a descending changes were observed at Amazon forest near San Ramon.

Drought and Ecological Flows in the Lower Guadiana River Basin (Southwest Iberian Peninsula)

Drought temporal characterization is a fundamental instrument in water resource management and planning of basins with dry-summer Mediterranean climate and with a significant seasonal and interannual variability of precipitation regime. This is the case for the Lower Guadiana Basin, where the river is the border between Spain and Portugal (Algarve-Baixo Alentejo-Andalucía Euroregion). For this transboundary basin, a description and evaluation of hydrological drought events was made using the Standardized Precipitation Index (SPI) with monthly precipitation time series of Spanish and Portuguese climatic stations in the study area. The results showed the occurrence of global cycles of about 25–30 years with predominance of moderate and severe drought events. It was observed that the current requirements of ecological flows in strategic water bodies were not satisfied in some months of October to April of years characterized by severe drought events occurring in the period from 1946 to 2015. Therefore, the characterization of the ecological status of the temporary streams that were predominant in this basin should be a priority in the next hydrologic plans in order to identify the relationships between actual flow regimes and habitat attributes, thereby improving environmental flows assessments, which will enable integrated water resource management.

Comparative analysis of probability distributions for the Standardized Precipitation Index and drought evolution in China during 1961–2015

As a representative index for monitoring and assessing drought, the Standardized Precipitation Index (SPI) relies on a suitable probability distribution function (PDF) to describe a precipitation series, which allows interregional comparisons following normalization. In this study, we considered nine PDFs (exponential (EXP), extreme value (EV), gamma (GAM), generalized Pareto (GP), logistic (LO), log-logistic (LOL), log-normal (LON), normal (NOR), and Weibull (WEI)) as candidates for use in SPI calculations. Based on monthly precipitation time series data (1961–2015) from 582 stations across China, together with the Kolmogorov–Smirnov (K–S) and Akaike Information Criterion (AIC) methods, differences in optimal PDFs for SPI calculations were compared comprehensively from the perspectives of timescale, record length, and index value. Based on the SPI calculated using the optimal PDF at the 6-month timescale (SPI6-opt), we analyzed the spatiotemporal characteristics of drought trends in China using the Mann–Kendall method. Results indicated both the timescale and the record length would affect the selection of the optimal PDF. The performance of the WEI and GAM distributions was superior to other distributions in describing monthly precipitation (especially for long precipitation records) at short and multiple timescales, respectively. During the entire study period, areas of China with high frequency of drought have transferred from the northwest (1960s), to the northeast (2000s), and to the southwest (most recent 5 years). Trend analysis revealed a noticeable wetting tendency confined mainly to Northwest, Northeast, and Southeast China and a significant trend toward drought in Southwest China and on the North China Plain.

Change features of time-series climate variables from 1962 to 2016 in Inner Mongolia, China

Detecting change features of climate variables in arid/semi-arid areas is essential for understanding related climate change patterns and the driving and evolution mechanism between climate and arid/semi-arid ecosystems. This paper takes Inner Mongolia of China, a unique arid/semi-arid ecosystem, as the study area. We first detected trend features of climate variables using the linear trend analysis method and then detected their trend-shift features using the breaks for additive seasonal and trend method based on the time-series of monthly precipitation and monthly mean temperature datasets from 1962 to 2016. We analyzed the different change features of precipitation and temperature on a regional scale and in different ecological zones to discover the spatial heterogeneity of change features. The results showed that Inner Mongolia has become warmer-wetter during the past 54 years. The regional annual mean temperature increased 0.4°C per decade with a change rate of 56.2%. The regional annual precipitation increased 0.07 mm per decade with a slightly change rate of about 1.7%, but the trend was not statistically significant. The warmer trend was contributed by the same positive trend in each season, while the wetter trend was contributed by the negative trend of the summer precipitation and the positive trend of the other three seasons. The regional monthly precipitation series had a trend-shift pattern with a structural breakpoint in the year 1999, while the regional monthly mean temperature series showed an increasing trend without a periodical trend-shift. After the year 2000, the warmer-wetter trend of the climate in Inner Mongolia was accelerated. The late 20th century was a key period, because the acceleration of the wetter trend in some local zones (I and II) and the alleviation of the warmer trend in some local zones (VII, VIII and IX) occurred simultaneously. Moreover, the change features had a strong spatial heterogeneity, the southeastern and southwestern of Inner Mongolia went through a warmer-drier trend compared with the other areas. The spatio-temporal heterogeneity of the climate change features is a necessary background for various types of research, such as regional climate change, the evolution of arid/semi-arid ecosystems, and the interaction mechanisms between climate and arid/semi-arid ecosystems based on earth-system models in Inner Mongolia.

Air temperature and precipitation analyses on a small Mediterranean island: the case of the remote island of Lastovo (Adriatic Sea, Croatia)

The paper analyses a series of annual, monthly, and daily air temperatures, and annual and monthly precipitation at the Lastovo meteorological station on the island of Lastovo (Croatia) during the 1948-2018 period. The small carbonate Adriatic island of Lastovo is the most remote inhabited island in the Croatian part of the Adriatic Sea. The absolute minimum annual air temperatures range between -6.8 °C (recorded in 1963) and 4.0 °C (recorded in 1974), with an average value of -1.2 °C. The Mean annual air temperatures range between 14.7 °C (recorded in 1980) and 17.4 °C (recorded in 2018), with an average value of 15.8 °C. The absolute maximum annual air temperatures range between 31.7 °C (recorded in 1959) and 38.3 °C (recorded in 1998), with an average value of 34.8 °C. A strong jump in the minimum annual temperatures started in 1972. The mean annual temperature jump began nine years later in 1981, while the maximum annual temperature jump occurred in 1992. The values of the t-test for all three analysed annual temperature indices (ATI) substantiate the conclusion that the average values in two subperiods defined by the RAPS method are statistically significant at the level p<<0.01. The increasing trend of air temperatures is significantly higher for the time series of the average maximum temperature Tmax than for the time series of the average minimum temperature Tmin. The most prominent increasing trend occurs in June and July. The number of warm and hot days in Lastovo has continuously increased over the analysed period. The increasing trends for both indices are statistically significant at the level p<0.01. The day-to-day (DTD) method established a decrease in the night-time to night-time variability, and an increase in the day-time to day-time temperature variability. The number of frost days is steadily decreasing. The average annual precipitation in the 1948-2018 period was 666 mm, while the minimum and the maximum precipitation was 368 mm and 1089 mm, respectively. No trends have been established for the annual and the monthly precipitation time-series during the 1948-2018 period, though a statistically insignificant drop in annual precipitation is found after 1982.

Homogeneity tests and non-parametric analyses of tendencies in precipitation time series in Keszthely, Western Hungary

This study aims to investigate the precipitation trends in Keszthely (Western Hungary, Central Europe) through an examination of historical climate data covering the past almost one and a half centuries. Pettitt’s test for homogeneity was employed to detect change points in the time series of monthly, seasonal and annual precipitation records. Change points and monotonic trends were analysed separately in annual, seasonal and monthly time series of precipitation. While no break points could be detected in the annual precipitation series, a significant decreasing trend of 0.2–0.7 mm/year was highlighted statistically using the autocorrelated Mann-Kendall trend test. Significant change points were found in those time series in which significant tendencies had been detected in previous studies. These points fell in spring and winter for the seasonal series, and October for the monthly series. The question therefore arises of whether these trends are the result of a shift in the mean. The downward and upward shift in the mean in the case of spring and winter seasonal amounts, respectively, leads to a suspicion that changes in precipitation are also in progress in these seasons. The study concludes that homogeneity tests are of great importance in such analyses, because they may help to avoid false trend detections.

Negative regional Standardized Precipitation Index trends prevail in the Mexico’s state of Zacatecas.

As the earth atmosphere warms, it is unclear how the precipitation will change or how these changes will impact regional rainfall. For the study of spatial and temporal variability of rainfall, several indexes have been developed. The Standardized Precipitation Index (SPI) that only involves recorded rainfall data has been used as a tool for climatic zone classif ication and a drought indicator. Then, the aims of the present study were: 1) to cluster monthly precipitation time series into groups that represent regions under the basis of similar precipitation regimes, 2) to compute regional SPI’s using all the members (time series) of each cluster, and 3) to estimate trends of the regional SPI’s. The cluster analysis approach was used to identify four groups of monthly precipitation time series that represent regions of similar precipitation regimes. Afterwards, regional SPI’s were estimated using all the members of each cluster. Finally, four regional SPI trends were estimated by means of the Mann-Kendall trend test and Sen’s slope estimator. Estimated decreasing SPI trends imply prevail of negative values at the end of the study period (1964-2014), which indicate less than median precipitation in the entire Zacatecas state territory. For instance, SPI at 12-month time scale Sen’s slope values were -0.17 and -0.18 for the wet and dry seasons, respectively in the Semi-desert region. Thus, the evidenced trends may be having influence on the availability of surface water, groundwater levels and aquifers recharge in the near future. So, it is imperative to adjust inhabitants’ activities according to design planned climate change adaptation strategies.