Tables are presented listing parameters and fit statistics for 25,453 maximum likelihood logistic regression (MLLR) models describing hydrological drought probabilities at 324 gaged locations on rivers and streams in the Delaware River Basin (DRB). Data from previous months are used to estimate chance of hydrological drought during future summer months. Models containing 1 explanatory variable use monthly mean daily streamflow data (DV) to provide hydrological drought streamflow probabilities for July, August, and September as functions of monthly mean DV from the previous 11 months. Outcomes are estimated 1 to 12 months ahead of their occurrence. Models containing 2 explanatory variables use monthly mean daily streamflow data (DV) and monthly mean precipitation data (P) to provide hydrological drought streamflow probabilities for July, August, and September as functions of monthly mean DV and monthly mean P from the previous October, November, December, January, and February. Outcomes are estimated 5 to 12 months ahead of their occurrence. Models containing 3 explanatory variables use monthly mean daily streamflow data (DV), monthly mean precipitation data (P), and monthly mean maximum daily air temperature (T) to provide hydrological drought streamflow probabilities for July, August, and September as functions of monthly mean DV, monthly mean P, and monthly mean maximum T from the previous October, November, December, January, and February. Outcomes are estimated 5 to 12 months ahead of their occurrence. Models containing 4 explanatory variables use monthly mean daily streamflow data (DV), monthly mean precipitation data (P), monthly mean maximum daily air temperature (T), and monthly mean potential evapotranspiration data (PET) to provide hydrological drought streamflow probabilities for July, August, and September as functions of monthly mean DV, monthly mean P, monthly mean maximum T, and monthly mean PET from the previous October, November, December, January, and February. Outcomes are estimated 5 to 12 months ahead of their occurrence. Explanatory variable selections for multiparameter models were optimized using random forest statistical methods. Selected single-parameter and multi-parameter models are provided. Overall correct classification rates tend to improve and models become more complex as the number of model explanatory variables increases from 1 to 4. Parameters for models with 1 explanatory variable are listed in the table labeled: “DRB-1_Variable_Equations.” Parameters for models with 2 explanatory variable are listed in the table labeled: “DRB-2_Variable_Equations.” Parameters for models with 3 explanatory variable are listed in the table labeled: “DRB-3_Variable_Equations.” Parameters for models with 4 explanatory variable are listed in the table labeled: “DRB-4_Variable_Equations.” Parameters describing models containing 1 explanatory variable may be used to populate drought probability equations as follows: p =1/[1 + e^-(β0+ β1• DV)] where: e is the base of the natural logarithm, β0 is an intercept parameter, β1 is a slope parameter, DV is a factor variable describing monthly mean daily streamflow (ft3/s). Parameters describing models containing 2 explanatory variables may be used to populate drought probability equations as follows: p =1/[1 + e^-(β0+ β1• DV+ β2• P)] where: e is the base of the natural logarithm, β0 is an intercept parameter, β1 is a slope parameter, β2 is a slope parameter, DV is a factor variable describing monthly mean daily streamflow (ft3/s), P is a factor variable describing monthly mean precipitation (in/day). Parameters describing models containing 3 explanatory variables may be used to populate drought probability equations as follows: p =1/[1 + e^-(β0+ β1• DV+ β2• P+ β3• T)] where: e is the base of the natural logarithm, β0 is an intercept parameter, β1 is a slope parameter, β2 is a slope parameter, β3 is a slope parameter DV is a factor variable describing monthly mean daily

Tables are presented listing parameters and fit statistics for 25,453 maximum likelihood logistic regression (MLLR) models describing hydrological drought probabilities at 324 gaged locations on rivers and streams in the Delaware River Basin (DRB). Data from previous months are used to estimate chance of hydrological drought during future summer months. Models containing 1 explanatory variable use monthly mean daily streamflow data (DV) to provide hydrological drought streamflow probabilities for July, August, and September as functions of monthly mean DV from the previous 11 months. Outcomes are estimated 1 to 12 months ahead of their occurrence. Models containing 2 explanatory variables use monthly mean daily streamflow data (DV) and monthly mean precipitation data (P) to provide hydrological drought streamflow probabilities for July, August, and September as functions of monthly mean DV and monthly mean P from the previous October, November, December, January, and February. Outcomes are estimated 5 to 12 months ahead of their occurrence. Models containing 3 explanatory variables use monthly mean daily streamflow data (DV), monthly mean precipitation data (P), and monthly mean maximum daily air temperature (T) to provide hydrological drought streamflow probabilities for July, August, and September as functions of monthly mean DV, monthly mean P, and monthly mean maximum T from the previous October, November, December, January, and February. Outcomes are estimated 5 to 12 months ahead of their occurrence. Models containing 4 explanatory variables use monthly mean daily streamflow data (DV), monthly mean precipitation data (P), monthly mean maximum daily air temperature (T), and monthly mean potential evapotranspiration data (PET) to provide hydrological drought streamflow probabilities for July, August, and September as functions of monthly mean DV, monthly mean P, monthly mean maximum T, and monthly mean PET from the previous October, November, December, January, and February. Outcomes are estimated 5 to 12 months ahead of their occurrence. Explanatory variable selections for multiparameter models were optimized using random forest statistical methods. Selected single-parameter and multi-parameter models are provided. Overall correct classification rates tend to improve and models become more complex as the number of model explanatory variables increases from 1 to 4. Parameters for models with 1 explanatory variable are listed in the table labeled: “DRB-1_Variable_Equations.” Parameters for models with 2 explanatory variable are listed in the table labeled: “DRB-2_Variable_Equations.” Parameters for models with 3 explanatory variable are listed in the table labeled: “DRB-3_Variable_Equations.” Parameters for models with 4 explanatory variable are listed in the table labeled: “DRB-4_Variable_Equations.” Parameters describing models containing 1 explanatory variable may be used to populate drought probability equations as follows: p =1/[1 + e^-(β0+ β1• DV)] where: e is the base of the natural logarithm, β0 is an intercept parameter, β1 is a slope parameter, DV is a factor variable describing monthly mean daily streamflow (ft3/s). Parameters describing models containing 2 explanatory variables may be used to populate drought probability equations as follows: p =1/[1 + e^-(β0+ β1• DV+ β2• P)] where: e is the base of the natural logarithm, β0 is an intercept parameter, β1 is a slope parameter, β2 is a slope parameter, DV is a factor variable describing monthly mean daily streamflow (ft3/s), P is a factor variable describing monthly mean precipitation (in/day). Parameters describing models containing 3 explanatory variables may be used to populate drought probability equations as follows: p =1/[1 + e^-(β0+ β1• DV+ β2• P+ β3• T)] where: e is the base of the natural logarithm, β0 is an intercept parameter, β1 is a slope parameter, β2 is a slope parameter, β3 is a slope parameter DV is a factor variable describing monthly mean daily

Tables are presented listing parameters used in logistic regression equations describing drought streamflow probabilities in the Northeastern United States. Streamflow daily data, streamflow monthly mean data, maximum likelihood logistic regression (MLLR) equation explanatory parameters, equation goodness of fit parameters, and Receiver Operating Characteristic (ROC) AUC values identifying the utility of each relation, describe each model of the probability (chance) of a particular streamflow daily value exceeding or not exceeding an identified drought streamflow threshold. These models are key inputs to drought forecasting web applications for the northeastern United states {https://usgs.maps.arcgis.com/apps/MapSeries/index.html?appid=b8c5da617a0e4d628e3e39f7dbd512da}

A table is presented listing: (1) USGS Gage Station Numbers, (2) Model Identification Tags, (3) Model Term Estimates, (4) Model Term Fit Statistics, and (5) Model Performance Indices for Maximum Likelihood Logistic Regression (MLLR) Models estimating hydrological drought probabilities in the United States. Models were developed using streamflow daily values (DV) readily available from the U.S. Geological Survey National Water Information System (NWIS) and mean monthly streamflows readily computed from NWIS streamflow DV. Models were prepared for 9,144 sites throughout the United States as described in: Modeling Summer Month Hydrological Drought Probabilities In The United States Using Antecedent Flow Conditions by Samuel H. Austin and David L. Nelms, JAWRA 1-14, https://doi.org/10.1111/1752-1688.12562.

Tables are presented listing parameters used in logistic regression equations describing drought streamflow probabilities in the Northeastern United States. Streamflow daily data, streamflow monthly mean data, maximum likelihood logistic regression (MLLR) equation explanatory parameters, equation goodness of fit parameters, and Receiver Operating Characteristic (ROC) AUC values identifying the utility of each relation, describe each model of the probability (chance) of a particular streamflow daily value exceeding or not exceeding an identified drought streamflow threshold. These models are key inputs to drought forecasting web applications for the northeastern United states {https://usgs.maps.arcgis.com/apps/MapSeries/index.html?appid=b8c5da617a0e4d628e3e39f7dbd512da}

A table is presented listing: (1) USGS Gage Station Numbers, (2) Model Identification Tags, (3) Model Term Estimates, (4) Model Term Fit Statistics, and (5) Model Performance Indices for Maximum Likelihood Logistic Regression (MLLR) Models estimating hydrological drought probabilities in the United States. Models were developed using streamflow daily values (DV) readily available from the U.S. Geological Survey National Water Information System (NWIS) and mean monthly streamflows readily computed from NWIS streamflow DV. Models were prepared for 9,144 sites throughout the United States as described in: Modeling Summer Month Hydrological Drought Probabilities In The United States Using Antecedent Flow Conditions by Samuel H. Austin and David L. Nelms, JAWRA 1-14, https://doi.org/10.1111/1752-1688.12562.

A table is presented listing: (1) USGS Gage Station Numbers, (2) Model Identification Tags, (3) Model Term Estimates, (4) Model Term Fit Statistics, and (5) Model Performance Indices for Maximum Likelihood Logistic Regression (MLLR) Models estimating hydrological drought probabilities in the United States. Models were developed using streamflow daily values (DV) readily available from the U.S. Geological Survey National Water Information System (NWIS) and mean monthly streamflows readily computed from NWIS streamflow DV. Models were prepared for 9,144 sites throughout the United States as described in: Modeling Summer Month Hydrological Drought Probabilities In The United States Using Antecedent Flow Conditions by Samuel H. Austin and David L. Nelms, JAWRA 1-14, https://doi.org/10.1111/1752-1688.12562.

A table is presented listing: (1) monthly streamflows, (2) drought duration dates, (3) drought severity indices, (4) supporting statistics, and (5) identification tags, for analysis of hydrological droughts in the Conterminous United States (CONUS). Data were summarized from USGS streamflow daily values (DV), readily available from the U.S. Geological Survey National Water Information System (NWIS), for USGS gage stations used in SIR 2017-#### Variability of Hydrological Droughts in the Conterminous United States, 1951 through 2014 by Samuel H. Austin, David M. Wolock, and David L. Nelms. http://dx.doi.org/10.3133/XXXXX.

A table is presented listing: (1) monthly streamflows, (2) drought duration dates, (3) drought severity indices, (4) supporting statistics, and (5) identification tags, for analysis of hydrological droughts in the Conterminous United States (CONUS). Data were summarized from USGS streamflow daily values (DV), readily available from the U.S. Geological Survey National Water Information System (NWIS), for USGS gage stations used in SIR 2017-#### Variability of Hydrological Droughts in the Conterminous United States, 1951 through 2014 by Samuel H. Austin, David M. Wolock, and David L. Nelms. http://dx.doi.org/10.3133/XXXXX.

This data release presents truth data and benchmark results describing simulation of hydrologic drought events in the conterminous United States. This data release supports a publication (Simeone and others, 2024) which documents drought benchmarking methods and their application to the results of the National Hydrologic Model Precipitation-Runoff Modeling System v1.0 (NHM-PRMS). Truth data used were observations at U.S. Geological Survey streamgages across the conterminous United States. These data include 4662 U.S. Geological Survey streamgages with a historical period from 1984-2016. The following files are included in this data release: 1) kappa_long_nhm.csv: Benchmark results for the Cohen's kappa evaluation metrics in long table format. 2) spear_bias_dist_long_nhm.csv: Benchmark results for the Spearman's, bias, and distributional evaluation metrics in long table format. 3) ann_eval_long_nhm.csv: Benchmark results for the annual drought evaluation metrics in long table format. 4) streamflow_percentiles_nhm.zip: A zip file containing individual streamflow percentile data files used in this analysis as truth data. 5) input_data_nhm.zip: A zip file with input data for individual streamgages used for our data analysis pipeline as truth data. 6) streamflow_gages_in_study.csv: Metadata information for the 4662 U.S. Geological Survey streamgages contained in the above datasets.