The lack of reliable and updated precipitation datasets is the most important limitation in the study of many climatological and hydrological subjects, including climate change and temporal variability of precipitation in many data sparse areas around the globe. This is particularly valid for Iran where vast areas of central-eastern country that host the Iranian deserts, suffer from an inadequate network of rain-gage stations, required for climatological studies. The highlands of the mountainous regions of western and northern Iran have the same problem and limited representative stations are available for high elevation areas of these regions. One of solution to overcome this obstacle is to use available gridded precipitation datasets that have proved their representativeness for many different parts of the world. Among many available precipitation datasets are the Global Precipitation Climatology Center (GPCC) and the Tropical Rainfall Measuring Mission (TRMM) that have been widely used in many researches, indicating their accurate estimation of precipitation values and intera-annual variation for the regions studied. The GPCC is a gage based dataset that is routinely creating through interpolation of worldwide precipitation stations combined with satellite records, whereas the TRMM is a purely remote sensed data developed by joint collaboration between NASA and the Japan Aerospace Exploration Agency (JAXA). The representativeness and performance of the GPCC and TRMM-3B43V7 precipitation datasets in estimating precipitation amounts at the locations of 46 Iranian synoptic stations distributed across the country is herein examined. Spatial resolutions of TRMM-3B43V7 and GPCC datasets used in this study are respectively 0.25 × 0.25 and 0.5 × 0.5 latitude and longitude. For each station, the closest grid point of each of the datasets to the station coordinates were chosen for statistically comparison analysis. To evaluate the performance of these datasets in comparison with the observed precipitation records at the considered locations we have used R squared, the Nash–Sutcliffe model efficiency coefficient, RMSE, Bias, B slope of the regression and the standardized RMSE indicators. The performances of the datasets were also graphically represented through scatter plots of the established regression between the observation and each of the two used datasets. The results of the statistical indicators were represented through plotting the indicators over the map of Iran to ease revealing spatial tendency of the indicators and explaining the possible geographical role in controlling the spatial variation of the indicators. The results revealed that both GPCC and TRMM-3B43V7 perform well in majority of the studied stations with strong correlation coefficients. However, it was found that the TRMM-3B43V7 underestimates precipitation in some stations located in the coastal areas of the Caspian Sea as well as in some stations along the Persian Gulf and the Oman seas, indicating that TRMM-3B43V7 is somewhat inefficient in adequately estimating precipitation in the coastal areas; which is very likely due to being unable to remove the effect of sea atmosphere interaction in stations nearby the seas. Contrarily, in some locations mostly situated in northwestern and northeastern mountainous areas of the country the TRMM-3B43V7 moderately over estimates the observed precipitation. Similarly, the GPCC well estimates precipitation in almost all stations with very high correlation coefficient and Nash–Sutcliffe model efficiency coefficient. Similar to TRMM-3B43V7, again it was found that the GPCC underestimates precipitation in most stations located along the coastal areas of the Caspian Sea. As for TRMM-3B43V7, the over-estimations of GPCC are mostly observed in northwestern Iran which is very likely due to not incorporating enough stations from high elevation areas of western Iran by the GPCC. On the whole, the results indicate that both datasets perform well in most locations of Iran and can be confidentially used in climatological and hydrological studies with or without the observation data. The results also indicate that the GPCC perform better in areas that share a denser network of stations with GPCC and vice versa. However, the very good results achieved with TRMM-3B43V7 that are completely independent from the observation indicates a promising future in having much improved remotely sensed precipitation records that well match the observed precipitation in very remote areas having no rain gages.

Aim: This study investigates the accuracy and reliability of GPCC and CRU data for conducting various hydroclimatic studies in Iraq. Material & Method: The data used include monthly precipitation data from the GPCC database with a spatial resolution of 0.5° and 0.25° geographical latitude and longitude, and monthly precipitation, minimum and maximum temperature data from the CRU database with a spatial resolution of 0.5° geographical latitude and longitude over the period 1990–2020. R, R2, EF, BIAS, RMSE, and Slope statistics were used to evaluate the accuracy of the data. Finding: According to the validation results, GPCC and CRU exhibit satisfactory accuracy in estimating precipitation minimum and maximum temperatures in Iraq. GPCC shows a high correlation coefficient (0.71-0.87) in estimating precipitation in Iraq's eastern and northern provinces. The Slope statistic ranges between 0.80 and 1.2, and the NRMSE is less than 3 in most stations. CRU demonstrates a high correlation coefficient (R≈0.81), EF>0.5, NRMSE<2, and Bias between -2 and 6 mm in estimating precipitation in the eastern and northern parts of the country. Furthermore, CRU exhibits high accuracy in estimating minimum and maximum temperature in Iraq; as such, the CRU data and observed data at stations exhibit strong agreement, with R>0.96, R2>0.95, EF>0.95, Slope>0.95, and NRMSE around less than 1.5 in most regions. Conclusion: Based on the validation results, GPCC and CRU have suitable accuracy in estimating rainfall minimum and maximum temperatures in Iraq. Therefore, GPCC and CRU data can be introduced as reliable sources for analyzing precipitation and temperature patterns and in areas lacking data in Iraq. Innovation: Both data sources provide the possibility of analyzing precipitation and temperature patterns, as well as important decision-making steps related to water management and adaptation to climate change.

Precipitation is a vital component of the global water and energy cycle with large variations in space and time. The observational datasets that are based on meteorological stations data usually serve as the main sources of precipitation data. However, because of uneven distribution in space, such datasets may not be directly applicable to some problems. Furthermore, there are gaps in the data as there may be times for which the precipitation has not been recorded by some metrological stations for various technical reasons. In the last decades, several gridded precipitation databases have been developed by researchers or institutes. The main aim of creating these databases is to serve user requirements and solve the problems mentioned above. The even distribution in space of the gridded precipitation data and their availability are two very important factors. These databases are critical for many studies including climate change and numerical weather prediction (NWP) applications, management of water resources, agriculture, and disaster management. The Global Precipitation Climatology Centre (GPCC) has been established in the year 1989 at the request of the World Meteorological Organization (WMO). It is constructed by the Deutscher Wetterdienst (DWD, National Meteorological Service of Germany) as a German contribution to the World Climate Research Programme (WCRP). The precipitation data of GPCC are freely available via the website http: //gpcc.dwd.de at 2.5º × 2.5º, 1º×1º, and 0.5 º × 0.5 º resolutions. The aim of this research is to evaluate the accuracy of GPCC database over Iran by comparing it with two national databases, the Asfezari and that of the synoptic stations called Stations hereafter. The monthly precipitation data from the three databases including GPCC, Asfezari and Stations have been used from January 1962 to the end of December 2010. To evaluate the accuracy of the estimated GPCC precipitation data, first the spatial resolutions of the three databases have been synchronized by the nearest neighbor algorithm. The high-resolution database is converted to the low-resolution database in order to select spatial pixels and carry out the comparisons. Seven accuracy evaluation indices have been used. The results indicate a high temporal correlation between the estimated precipitation of GPCC and the observed precipitation by the Asefazri and the Stations databases. The results of applying accuracy evaluation indices to the precipitation time series show that in addition to high temporal correlation, quantitatively the estimated precipitations are also very similar to the observed precipitations. Although in some regions the estimated precipitation values are contaminated with bias, but overall the estimated precipitation error is low compared to the total precipitation received. In a spatial viewpoint, the highest accuracy is observed over the western parts of Zagros mountain range and the northeast of the country. Over these regions, the index of agreement and the coefficient of determination are close to unity. The highest relative root mean square (rms) is observed over the dry interior regions and the Lut desert. The relative rms is low in the regions with high precipitation when compared with the rest of the regions. In a temporal viewpoint, the highest correlation between the precipitation time series is observed in rainy months. Based on the results from the Nash–Sutcliffe efficiency index, over most regions of the country, using the estimated precipitations by GPCC is preferable to applying the mean precipitation amounts. The results of this study confirmed the finding of other researches about the accuracy of the estimated precipitation by GPCC database.

Precipitation plays an important role in the global energy and water cycle. Special interest in long-term precipitation analyses arises from the need to assess climate change and its impacts on all spatial scales. Based on this demand national and international organization initiated and support many research and monitoring programs. There are a lot of centers developing gridded precipitation data in various spatial and temporal scales and make them available freely. In this paper we want to introduce national database alongside CMAP, GPCP and GPCC datasets and then compare them with national database for Iran. The comparison showed us that there is a high coefficient of correlation between Asfazari and the other database especially GPCC during rainy seasons this correlation was very high.

In this research, for analysis the spatial- temporal trend of precipitation in Caspian basin, the monthly data of GPCC data base in the spatial resolution of 0.5°×0.5° during 63 years period(1951- 2013) have been used. In order to analyze of the spatial- temporal trend of precipitation, the mean of annual, seasonal and monthly amounts of precipitation were prepared and then by applying Mann-Kendall nonparametric test at 0.95 level of significance, 17 maps precipitation spatial trend were produced in GIS. To examine the temporal trend of precipitation, first 17 graphs were prepared using the weighted amount of precipitation and then the trend was evaluated using Mann-Kendall nonparametric test at 0.95 level of significance. The evaluation of monthly precipitation spatial trend maps indicated a significant downward trend at 0.95 level of significance in March and April on the western, eastern and southwest regions of Caspian basin. Only in spring and autumn there have been a significant downward trend and in none of the other seasons no meaningful trend was detected. Also, the analysis of yearly precipitation spatial trend illustrated a significant downward trend on western region. The analysis of precipitation revealed significant downward trend on monthly time series at the 0.95 level of significance in March on Caspian basin.

Background and objectives: Drought is a natural event that faces many countries with water shortage every year. Arid and semi-arid climate and improper distribution of rainfall in terms of space and time have increased the negative effect of water resources in Iran. In the present study, the meteorological drought condition of the Urmia Lake catchment was investigated using network data of precipitation, temperature and evapotranspiration in 10 synoptic stations from 1955- 2019. For this purpose, the performance of network data of CRU (Climatic Research Unit) and GPCC (Global Precipitation Climatology Center) in estimating climatic parameters using ground data was evaluated. Then, network data was used to calculate RDI (Reconnaissance Drought Index) and drought monitoring was observed and analyzed during the statistical period study.Materials and methods: The watershed is located in the northwest of Iran in the provinces of West and East Azerbaijan and Kurdistan, and its western border is the heights of Iran and Turkey. In the present research, at first, by referring to the Meteorological Organization of the Iran, the rainfall and temperature data of the studied stations were received and processed during a period of 65 years (1955-2019). Then, in order to introduce and use network data for places and times without statistics, from monthly precipitation data GPCC and (minimum, average and maximum) temperature components of CRU was used for 10 selected synoptic stations of Urmia Lake basin during the statistical period. In order to analyze the long-term drought and calculate the RDI index, the data of precipitation, temperature and evapotranspiration obtained from the network data were used.Results: In this study, two general approaches were used to calibrate the data used. The first is that all ground data extracted from meteorological stations are drawn in against the network data and a regression relationship is fitted to them. In the second, the monthly changes of the network data are taken into consideration and the calibration is done for each month separately. Therefore, monthly recalibration was done in all stations for the available data and the results showed that the calculation error in the CRU data for temperature and evapotranspiration was much smaller and the temperature data values were able to estimate evapotranspiration with less error and better performance. For example, in Urmia station for ETo estimation, the value of the RMSE evaluation index between ground and CRU data is 0.918 mm/ day. While after calibration, this value decreased to 0.671 mm/day. This process of reducing the error between ground data and CRU has been repeated in all the stations studied. In Piranshahr and Saqez stations, the estimation of reference evapotranspiration was associated with more error than other stations. So that the MAE standard in the mentioned stations before calibration was 1.087 and 0.965 mm/day, respectively, and after calibration, this index decreased to 0.309 and 0.467 mm/day. In this section, in addition to statistical indicators, a violin chart was also used to show the data distribution. In general, it can be concluded that the climate data obtained from the CRU and GPCC databases show a good agreement with the time values, but the bias correction in them should always be considered.Conclusion: The analysis of droughts in the catchment area indicates that from 1998-2019, RDI has more negative values, which indicates severe drought, and in other words, human activities and climatic conditions in the region with It faces a crisis. The results of the present study show the appropriate performance of CRU and GPCC network data in estimating hydrological parameters and it is recommended to use the above databases in areas where long-term ground data is not available.

Temperature and rainfall affect the quantity and quality of agricultural products. Therefore, it is important to estimate its spatial-temporal changes. In many region of the country, due to the low density of meteorological stations or the small statistical period of new stations, limited time and space information is available. Therefore, this study aims to use the data of CRU, AgMERRA, AgCFSR and GPCC gridded weather datasets in estimation of yield and water requirement of wheat and comparing with the estimated values with the information of Qazvin Synoptic Station. For this purpose, monthly weather time series of Qazvin synoptic station were extracted from 1980 to 2010 along with the data from the selected gridded datasets extracted from the closest grid cell to the synoptic station (K1), the average of four closest grid cells to the synoptic station (K4), and the average of eight closest grid cells to the synoptic station (K8). The quality of the gridded datasets was assessed with four statistical indices (R2, RMSE, NRMSE, ME) in indirect way (the latter using the outputs of the AquaCrop model). In estimating wheat water requirement, GPCC database with four points (K4) and one point (K1) showed the best performance. Wheat yield simulated with AgMERRA data with K1 and K4 closest grid cells had the highest correlation with the simulated values with data of synoptic station. Results showed that selected gridded datasets can be used to simulated grain yield, but only data from GPCC-CUR would result in reliable estimation of water requirement.

Precipitation is one of the most important factors used in estimating many hydrological parameters at the level of the catchment area. Due to the importance of precipitation data in various sciences and the absence of a large and adequate rainfall network, it is necessary to estimate precipitation data in some way. One way to estimate precipitation is to use satellite data. In this study, precipitation data of GPCC, GPCP, CMAP and NCEP-NCAR models with station data in Alborz, Qazvin, Zanjan, Kurdistan and Hamedan provinces were investigated. The results showed that GPCP, GPCC, CMAP and NCEP-NCAR had good results in these regions and among them GPCP and GPCC produced better results. In evaluating GPCP with the weighted average of stations in the study area in pixel 3 in 2003 Explanation Coefficient (R2), Model Efficiency Coefficient (EF), Averaged Error Error (MBE), Absolute Mean Error (MAE) and Root Mean Square Error (RAD) RMSE) were 0. 96, 0. 94, 3. 13, 5. 30 and 6. 58 mm / month, respectively.

This study aimed to present Climatic Research Unit (CRU) data and assess the accuracy of this data with the latest GPCC updates against the synoptic stations data during 2014-1985. For this purpose, monthly precipitation from GPCC Full Data Version 7, monthly precipitation and temperature from CRU TS3. 23 with 0. 5 × 0. 5 degree spatial resolution, monthly total precipitation, and average of minimum, maximum and mean daily temperature of 88 synoptic stations with proper distribution in Iran were gathered and analyzed. To assess the databases that mentioned R square, RMSE, IA, Bias, slope of the regression and EF indicators was used. Statistical comparisons revealed that, although accuracy of CRU precipitation dada in Iran, especially in the western half of country, is well, but in some regions, especially coastal areas, is less reliable. Because the R square in these areas is less than 0. 5, while the accuracy of GPCC data in all areas of Iran even coastal stations is very high. So, the amount of its precipitation in different regions of Iran is very close to the amount of precipitation recorded in the synoptic stations. Temperature components (average, minimum and maximum) assessment shows that although CRU amounts in the Caspian coastal area have more deviation than other areas, but, more than 95% of the observed data variance across the country explained by CRU and most of the CRU data distributed around the regression line. This term represents very high accuracy and reliability of CRU gridded temperature data in Iran. Based on the results of this study suggested that in the regions with no data, GPCC precipitation data and CRU temperature data to be used.