##Araghi, A., Martinez, C. J., Olesen, J. E., &Hoogenboom, G. 2022. Assessment of nine gridded temperature data for modeling of wheat production systems. Computers and Electronics in Agriculture, 199: 107189. https://doi.org/10.1016/j.compag.2022.107189.##Badescu, V., 2008. Modeling soalr radiation at the earth surface. Verlag Berlin Heidelberg. Springer.##Bannayan, M., Sanjani, S., Alizadeh, A., Lotfabadi, S and Mohammadian, A. 2010. Association between climate indices aridity index, and rainfed crop yield in northeast of Iran. Field Crops Research. 118(2): 105–114.##Battisti, R., Bender, F.D., and Sentelhas, P.C. 2019. Assessment of different gridded weather data for soybean yield simulations in Brazil. Theoretical and applied climatology, 135(1): 237-247.##Bazrafshan, J., Aghashariatmadary, Z., & Kamali, S. 2023. Evaluation of Quantile Mapping Methods in Bias Correction of the TRMM Satellite’s Estimated Precipitation Data During Vegetation Growth Period (Apr-Oct) in Kermanshah Province. Journal of Agricultural Meteorology, 10(2): 4-16. doi: 10.22125/agmj.2022.289694.1117. (In Farsi)##Belda, M., Holtanová, E., Halenka, T. and Kalvová, J. 2014. Climate classification revisited: from Köppen to Trewartha. Climate research, 59(1): 1-13.##Bender, F. D., & Sentelhas, P. C. 2018. Solar radiation models and gridded databases to fill gaps in weather series and to project climate change in Brazil. Advances in Meteorology, 2018(1): 6204382.##Burhan, A., and Athar, H. 2019. PDF based seasonal changes in AgMERRA observations and GCM20 and RegCM4.3 projections over Pakistan Region. Climate Change, 5(17): 68-81.##Ceglar, A., Toreti, A., Balsamo, G., & Kobayashi, S. 2017. Precipitation over monsoon Asia: a comparison of reanalyses and observations. Journal of Climate, 30(2): 465-476.##Emad Mohammad, A.Y and Darand, M. 2021. Evaluation of time-spatial accuracy of precipitation and temperature estimation of ERA5 database over Iraq. University of Kurdistan. (In Farsi)##Farzandi, M., Sanaeinejad, S. H., Ghahraman, B., & Sarmad, M. 2019. Imputation of Missing Meteorological Data with Evolutionary and Machine Learning Methods Case Study: Long-term Monthly Precipitation and Temperature of Mashhad. Water and Soil, 33(2): 361-377. doi: 10.22067/jsw. v33i2.74125. (In Farsi)##Forsythe, N., Blenkinsop, S. and Fowler, H.J. 2015. Exploring objective climate classification for the Himalayan arc and adjacent regions using gridded data sources. Earth System Dynamics, 6(1): 311-326.##Gilbert, R. O. 1987. Statistical methods for environmental pollution monitoring. John Wiley & Sons.##Gomis-Cebolla, J., Rattayova, V., Salazar-Galán, S., & Francés, F. 2023. Evaluation of ERA5 and ERA5-Land reanalysis precipitation datasets over Spain (1951–2020). Atmospheric Research, 284: 106606.##Haylock, M.R., Hofstra, N., Klein Tank, A.M.G., Klok, E.J., Jones, P.D. and New, M. 2008. A European daily high‐resolution gridded data set of surface temperature and precipitation for 1950–2006. Journal of Geophysical Research: Atmospheres, 113(D20).##Hirsch, R. M., Slack, J. R., & Smith, R. A. 1982. Techniques of trend analysis for monthly water quality data. Water resources research, 18(1): 107-121.##Hoseeni, Z. S., Moghaddasi, M., & Paimozd, S. 2022. Accuracy Assessment of ERA5 Datasets in Prediction of Climate Data and Drought Monitoring of Garechai Basin of Markazi Province. Iranian Journal of Soil and Water Research, 53(4): 715-732. doi: 10.22059/ijswr.2022.340295.669227. (In Farsi)##Javanshiri, Z., Asadi Oskouei, E., Flamarzi, Y., & Abasi, F. 2023. Accuracy assessment of CFS-v2, MERRA-2, ERA-5 temperature over the different regions of Iran. Iranian Journal of Geophysics, 17(4): 1-24. doi: 10.30499/ijg.2022.360882.1452. (In Farsi)##Jolliff, J. K., Kindle, J. C., Shulman, I., Penta, B., Friedrichs, M. A., Helber, R., & Arnone, R. A. 2009. Summary diagrams for coupled hydrodynamic-ecosystem model skill assessment. Journal of Marine Systems, 76(1-2): 64-82.##Jowkar, L., Panahi, F., Sadatinejad, S. J., & Shakiba, A. 2021. Precipitation Extremes Variability Trend in Bakhtegan Catchment Using AgMERRA and Stations Data. Irrigation and Water Engineering, 12(1): 364-381. doi: 10.22125/iwe.2021.138351. (In Farsi)##Kendall, M.G. 1975. Rank Correlation Methods. 4th Edition, Charles Griffin, London.##Lashkari, A., Bannayan Aval, M., Koocheki, A., Alizadeh, A., Choi, Y. S., & Park, S. 2016. Applicability of AgMERRA forcing dataset forgap-filling of in-situ meteorological observation, Case Study: Mashhad Plain. Water and Soil, 29(6): 1749-1758. doi: 10.22067/jsw. v29i6.41686. (In Farsi)##Liu, R., Zhang, X., Wang, W., Wang, Y., Liu, H., Ma, M., & Tang, G. 2024. Global-scale ERA5 product precipitation and temperature evaluation. Ecological Indicators, 166: 112481.##Maggioni, V., Sapiano, M. R., Adler, R. F., Tian, Y., & Huffman, G. J. 2014. An error model for uncertainty quantification in high-time-resolution precipitation products. Journal of Hydrometeorology, 15(3): 1274-1292. Mann, H. B. 1945. Nonparametric tests against trend. Econometrica: Journal of the econometric society, 245-259.##Poméon, T., Jackisch, D., and Diekkrüger, B. 2017. Evaluating the performance of remotely sensed and reanalysed precipitation data over West Africa using HBV light. Journal of hydrology. 547: 222-235.##Ramezani Etedali, H., Gorgin, F., & Kakvand, P. 2022. Study of the performance of two meteorological datasets in estimating the maize water footprint, a case study: Qazvin Plain. Iranian Journal of Irrigation & Drainage, 15(6): 1394-1403. (In Farsi)##Razavi, A. R., Nassiri Mahallati, M., Koocheki, A., & Beheshti, A. 2018. Applicability of AgMERRA for Gap-Filling of Afghanistan in-situ Temperature and Precipitation Data. Water and Soil, 32(3): 601-616. doi: 10.22067/jsw. v32i3.68501. (In Farsi) ##Rosenzweig, C., Elliott, J., Deryng, D., Ruane, A. C., Müller, C., Arneth, A., ... & Jones, J. W. 2014. Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proceedings of the national academy of sciences, 111(9): 3268-3273.##Salehnia, N., Alizadeh, A., Sanaeinejad, H., Bannayan, M., Zarrin, A., and Hoogenboom, G. 2017. Estimation of meteorological drought indices based on AgMERRA precipitation data and station-observed precipitation data. Journal of arid land, 9(6): 797-809. https://doi.org/10.1007/s40333-017-0070-y. (In Farsi)##Sen, P. K. 1968. Asymptotically efficient tests by the method of n rankings. Journal of the Royal Statistical Society Series B: Statistical Methodology, 30(2): 312-317.##Tang, X., Zhang, J., Gao, C., Ruben, G. B., and Wang, G. 2019. Assessing the uncertainties of four precipitation products for SWAT modeling in Mekong River Basin. Remote Sensing, 11(3): 304. doi:10.3390/rs11030304.##Taylor, R. 1990. Interpretation of the correlation coefficient: a basic review. Journal of diagnostic medical sonography, 6(1): 35-39.##Theil, H. 1950. A rank-invariant method of linear and polynomial regression analysis. Indagationes mathematicae, 12(85): 173.##Toreti, A., Maiorano, A., De Sanctis, G., Webber, H., Ruane, A. C., Fumagalli, D., Fumagalli, A. Ceglar, S. Niemeyer, and Zampieri, M. 2019. Using reanalysis in crop monitoring and forecasting systems. Agricultural systems, 168: 144- 153.##Zandi, R. 2017. Climate classification of Khorasan-Razavi province by Dumartin method using geographic information system. Journal of Geographical New Studies, Architecture and urbananism. 10(1): 21-34. http://noo.rs/jrnQp. (In Farsi)##Zhu, Q., Xuan, W., Liu, L., Xu, Y. P. 2016. Evaluation and hydrological application of precipitation estimates derived from PERSIANN‐CDR, TRMM 3B42V7, and NCEP‐CFSR over humid regions in China. Hydrological Processes, 30(17): 3061-3083. ##